Sample records for remote sensing methods

This book presents the theory and methods of GNSS remotesensing as well as its applications in the atmosphere, oceans, land and hydrology. It contains detailed theory and study cases to help the reader put the material into practice.

Remotesensing techniques are valuable for locating, assessing, and monitoring desertification. Remotelysensed data provide a permanent record of the condition of the land in a format that allows changes in land features and condition to be measured. The annotated bibliography of 118 items discusses remotesensingmethods that may be applied to desertification studies.

This book provides a comprehensive overview of the state of the art in the field of thermal infrared remotesensing. Temperature is one of the most important physical environmental variables monitored by earth observing remotesensing systems. Temperature ranges define the boundaries of habitats on our planet. Thermal hazards endanger our resources and well-being. In this book renowned international experts have contributed chapters on currently available thermal sensors as well as innovative plans for future missions. Further chapters discuss the underlying physics and image processing techni

the need for harmonised forest information can be satisfied using remotesensingmethods. In conclusion, the study showed that it is possible to derive harmonised forest information of high spatial detail in Europe with remotesensing. The study also highlighted the imperative provision of accuracy...

Multiscale segmentation of images can effectively form boundaries of different objects with different scales. However, for the remotesensing image which widely coverage with complicated ground objects, the number of suitable segmentation scales, and each of the scale size is still difficult to be accurately determined, which severely restricts the rapid information extraction of the remotesensing image. A great deal of experiments showed that the normalized difference vegetation index (NDVI) can effectively express the spectral characteristics of a variety of ground objects in remotesensing images. This paper presents a method using NDVI assisted adaptive segmentation of remotesensing images, which segment the local area by using NDVI similarity threshold to iteratively select segmentation scales. According to the different regions which consist of different targets, different segmentation scale boundaries could be created. The experimental results showed that the adaptive segmentation method based on NDVI can effectively create the objects boundaries for different ground objects of remotesensing images.

RemoteSensing provides information on how remotesensing relates to the natural resources inventory, management, and monitoring, as well as environmental concerns. It explains the role of this new technology in current global challenges. "RemoteSensing" will discuss remotelysensed data application payloads and platforms, along with the methodologies involving image processing techniques as applied to remotelysensed data. This title provides information on image classification techniques and image registration, data integration, and data fusion techniques. How this technology applies to natural resources and environmental concerns will also be discussed.

Full Text Available China's long-term planning major projects "high-resolution earth observation system" has been invested nearly 100 billion and the satellites will reach 100 to 2020. As to 2/3 of China's area covered by mountains，it has a higher demand for remotesensing. In addition to light intensity, frequency, phase, polarization is also the main physical characteristics of remotesensing electromagnetic waves. Polarization is an important component of the reflected information from the surface and the atmospheric information, and the polarization effect of the ground object reflection is the basis of the observation of polarization remotesensing. Therefore, the effect of eliminating the polarization effect is very important for remotesensing applications. The main innovations of this paper is as follows: (1 Remotesensing observation method. It is theoretically deduced and verified that the polarization can weaken the light in the strong light region, and then provide the polarization effective information. In turn, the polarization in the low light region can strengthen the weak light, the same can be obtained polarization effective information. (2 Polarization effect of vegetation. By analyzing the structure characteristics of vegetation, polarization information is obtained, then the vegetation structure information directly affects the absorption of biochemical components of leaves. (3 Atmospheric polarization neutral point observation method. It is proved to be effective to achieve the ground-gas separation, which can achieve the effect of eliminating the atmospheric polarization effect and enhancing the polarization effect of the object.

This volume debuts the new scope of RemoteSensing, which was first defined as the analysis of data collected by sensors that were not in physical contact with the objects under investigation (using cameras, scanners, and radar systems operating from spaceborne or airborne platforms). A wider characterization is now possible: RemoteSensing can be any non-destructive approach to viewing the buried and nominally invisible evidence of past activity. Spaceborne and airborne sensors, now supplemented by laser scanning, are united using ground-based geophysical instruments and undersea remotesensing, as well as other non-invasive techniques such as surface collection or field-walking survey. Now, any method that enables observation of evidence on or beneath the surface of the earth, without impact on the surviving stratigraphy, is legitimately within the realm of RemoteSensing. The new interfaces and senses engaged in RemoteSensing appear throughout the book. On a philosophical level, this is about the landscap...

We present two linearization methods for a pseudo-spherical atmosphere and general viewing geometries. The first approach is based on an analytical linearization of the discrete ordinate method with matrix exponential and incorporates two models for matrix exponential calculation: the matrix eigenvalue method and the Pade approximation. The second method referred to as the forward-adjoint approach is based on the adjoint radiative transfer for a pseudo-spherical atmosphere. We provide a compact description of the proposed methods as well as a numerical analysis of their accuracy and efficiency.

Full Text Available Oceans/Seas are important components of Earth that are affected by global warming and climate change. Recent studies have indicated that the deeper oceans are responsible for climate variability by changing the Earth’s ecosystem; therefore, assessing them has become more important. Remotesensing can provide sea surface data at high spatial/temporal resolution and with large spatial coverage, which allows for remarkable discoveries in the ocean sciences. The deep layers of the ocean/sea, however, cannot be directly detected by satellite remote sensors. Therefore, researchers have examined the relationships between salinity, height, and temperature of the oceans/Seas to estimate their subsurface water temperature using dynamical models and model-based data assimilation (numerical based and statistical approaches, which simulate these parameters by employing remotelysensed data and in situ measurements. Due to the requirements of comprehensive perception and the importance of global warming in decision making and scientific studies, this review provides comprehensive information on the methods that are used to estimate ocean/sea subsurface water temperature from remotely and non-remotelysensed data. To clarify the subsurface processes, the challenges, limitations, and perspectives of the existing methods are also investigated.

Requirements for a basic course in remotesensing to accommodate the needs of the graduate level and professional geologist are described. The course should stress the general topics of basic remotesensing theory, the theory and data types relating to different remotesensing systems, an introduction to the basic concepts of computer image processing and analysis, the characteristics of different data types, the development of methods for geological interpretations, the integration of all scales and data types of remotesensing in a given study, the integration of other data bases (geophysical and geochemical) into a remotesensing study, and geological remotesensing applications. The laboratories should stress hands on experience to reinforce the concepts and procedures presented in the lecture. The geologist should then be encouraged to pursue a second course in computer image processing and analysis of remotelysensed data.

Remotesensing is a technology that engages electromagnetic sensors to measure and monitor changes in the earth's surface and atmosphere. Normally this is accomplished through the use of a satellite or aircraft. This book, in its 3rd edition, seamlessly connects the art and science of earth remotesensing with the latest interpretative tools and techniques of computer-aided image processing. Newly expanded and updated, this edition delivers more of the applied scientific theory and practical results that helped the previous editions earn wide acclaim and become classroom and industry standa

The increasing length of sewage pipelines, and concomitant risk of leaks due to urban and industrial growth and development is exposing the surrounding land to contamination risk and environmental harm. It is therefore important to locate such leaks in a timely manner, to minimize the damage. Advances in active remotesensing Ground Penetrating Radar (GPR) and Frequency Domain Electromagnetic (FDEM) technologies was used to identify leaking potentially responsible for pollution and to identify minor spills before they cause widespread damage. This study focused on the development of these electromagnetic methods to replace conventional acoustic methods for the identification of leaks along sewage pipes. Electromagnetic methods provide an additional advantage in that they allow mapping of the fluid-transport system in the subsurface. Leak-detection systems using GPR and FDEM are not limited to large amounts of water, but enable detecting leaks of tens of liters per hour, because they can locate increases in environmental moisture content of only a few percentage along the pipes. The importance and uniqueness of this research lies in the development of practical tools to provide a snapshot and monitoring of the spatial changes in soil moisture content up to depths of about 3-4 m, in open and paved areas, at relatively low cost, in real time or close to real time. Spatial measurements performed using GPR and FDEM systems allow monitoring many tens of thousands of measurement points per hectare, thus providing a picture of the spatial situation along pipelines and the surrounding. The main purpose of this study was to develop a method for detecting sewage leaks using the above-proposed geophysical methods, since their contaminants can severely affect public health. We focused on identifying, locating and characterizing such leaks in sewage pipes in residential and industrial areas.

This book documents the state of the art in the use of remotesensing to address time-sensitive information requirements. Specifically, it brings together a group of authors who are both researchers and practitioners, who work toward or are currently using remotesensing to address time-sensitive information requirements with the goal of advancing the effective use of remotesensing to supply time-sensitive information. The book addresses the theoretical implications of time-sensitivity on the remotesensing process, assessments or descriptions of methods for expediting the delivery and improving the quality of information derived from remotesensing, and describes and analyzes time-sensitive remotesensing applications, with an emphasis on lessons learned. This book is intended for remotesensing scientists, practitioners (e.g., emergency responders or administrators of emergency response agencies), and students, but will also be of use to those seeking to understand the potential of remotesensing to addres...

A synthesis of more than ten years of experience, RemoteSensing Image Fusion covers methods specifically designed for remotesensing imagery. The authors supply a comprehensive classification system and rigorous mathematical description of advanced and state-of-the-art methods for pansharpening of multispectral images, fusion of hyperspectral and panchromatic images, and fusion of data from heterogeneous sensors such as optical and synthetic aperture radar (SAR) images and integration of thermal and visible/near-infrared images. They also explore new trends of signal/image processing, such as

Addressing the need for updated information in remotesensing, Introduction to RemoteSensing, Second Edition provides a full and authoritative introduction for scientists who need to know the scope, potential, and limitations in the field. The authors discuss the physical principles of common remotesensing systems and examine the processing, interpretation, and applications of data. This new edition features updated and expanded material, including greater coverage of applications from across earth, environmental, atmospheric, and oceanographic sciences. Illustrated with remotelysensed colo

Filling Gaps in RemotelySensed River Data Jonathan M. Nelson US Geological Survey National Research Program Geomorphology and Sediment Transport...the research work carried out under this grant are to develop and test two methods for filling in gaps in remotelysensed river data. The first...information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215

In this paper, we reviewed the principle, data, methods and steps in suspended sediment research by using remotesensing, summed up some representative models and methods, and analyzes the deficiencies of existing methods. Combined with the recent progress of remotesensing theory and application in water suspended sediment research, we introduced in some data processing methods such as atmospheric correction method, adjacent effect correction, and some intelligence algorithms such as neural networks, genetic algorithms, support vector machines into the suspended sediment inversion research, combined with other geographic information, based on Bayesian theory, we improved the suspended sediment inversion precision, and aim to give references to the related researchers.

The traditional methods of detecting ship targets in remotesensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remotesensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remotesensing images.

Utilizing high-resolution remotesensing images for earth observation has become the common method of land use monitoring. It requires great human participation when dealing with traditional image interpretation, which is inefficient and difficult to guarantee the accuracy. At present, the artificial intelligent method such as deep learning has a large number of advantages in the aspect of image recognition. By means of a large amount of remotesensing image samples and deep neural network models, we can rapidly decipher the objects of interest such as buildings, etc. Whether in terms of efficiency or accuracy, deep learning method is more preponderant. This paper explains the research of deep learning method by a great mount of remotesensing image samples and verifies the feasibility of building extraction via experiments.

Optical remotesensing relies on exploiting multispectral and hyper spectral imagery possessing high spatial and spectral resolutions respectively. These modalities, although useful for most remotesensing tasks, often present challenges that must be addressed for their effective exploitation. This book presents current state-of-the-art algorithms that address the following key challenges encountered in representation and analysis of such optical remotelysensed data: challenges in pre-processing images, storing and representing high dimensional data, fusing different sensor modalities, patter

remotesensing from satellites. Sensing of oceanographic variables from aircraft began with the photographing of waves and ice. Since then remote measurement of sea surface temperatures and wave heights have become routine. Sensors tested for oceanographic applications include multi-band color cameras, radar scatterometers, infrared spectrometers and scanners, passive microwave radiometers, and radar imagers. Remotesensing has found its greatest application in providing rapid coverage of large oceanographic areas for synoptic and analysis and

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remotesensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotelysensed datasets are discussed. Remotesensing indirect methods are subdivided into two categories of passive and active remotesensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotelysensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels.

'Remotesensing' is a generic term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotelysensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth's surface. Remotelysensed data are fundamental to geographic science. The Eastern Geographic Science Center (EGSC) of the U.S. Geological Survey (USGS) is currently conducting and promoting the research and development of three different aspects of remotesensing science: spectral analysis, automated orthorectification of historical imagery, and long wave infrared (LWIR) polarimetric imagery (PI).

The separation-combination method a new kind of analysis method of linear structures in remotesensing image interpretation is introduced taking northwestern Fujian as the example, its practical application is examined. The practice shows that application results not only reflect intensities of linear structures in overall directions at different locations, but also contribute to the zonation of linear structures and display their space distribution laws. Based on analyses of linear structures, it can provide more information concerning remotesensing on studies of regional mineralization laws and the guide to ore-finding combining with mineralization

Finding the change in multi-temporal remotesensing image is important in many the image application. Because of the infection of climate and illumination, the texture of the ground object is more stable relative to the gray in high-resolution remotesensing image. And the texture features of Local Binary Patterns (LBP) and Speeded Up Robust Features (SURF) are outstanding in extracting speed and illumination invariance. A method of change detection for matched remotesensing image pair is present, which compares the similarity by LBP and SURF to detect the change and unchanged of the block after blocking the image. And region growing is adopted to process the block edge zone. The experiment results show that the method can endure some illumination change and slight texture change of the ground object.

The remotesensing image has been widely used in AutoCAD, but AutoCAD lack of the function of remotesensing image processing. In the paper, ObjectARX was used for the secondary development tool, combined with the Image Engine SDK to realize remotesensing image pixel attribute data acquisition in AutoCAD, which provides critical technical support for AutoCAD environment remotesensing image processing algorithms.

@@In this paper, some image processing methods such as directional template (mask) matching enhancement, pseudocolor or false color enhancement, K-L transform enhancement are used to enhance a geological structure, one of important ore-controlling factors, shown in the remote-sensing images.This geological structure is regarded as image anomaly in the remote-sensing image, since considerable differences, based on the spatial spectral distribution pattern, in gray values (spectral), color tones and texture, are always present between the geological structure and background. Therefore,the enhancement of the geological structure in the remotesensing image is that of the spectral spatial difference.

Hyperspectral remotesensing is an emerging, multidisciplinary field with diverse applications that builds on the principles of material spectroscopy, radiative transfer, imaging spectrometry, and hyperspectral data processing. This book provides a holistic treatment that captures its multidisciplinary nature, emphasizing the physical principles of hyperspectral remotesensing.

Image clarity, which reflects the sharpness degree at the edge of objects in images, is an important quality evaluate index for optical remotesensing images. Scholars at home and abroad have done a lot of work on estimation of image clarity. At present, common clarity-estimation methods for digital images mainly include frequency-domain function methods, statistical parametric methods, gradient function methods and edge acutance methods. Frequency-domain function method is an accurate clarity-measure approach. However, its calculation process is complicate and cannot be carried out automatically. Statistical parametric methods and gradient function methods are both sensitive to clarity of images, while their results are easy to be affected by the complex degree of images. Edge acutance method is an effective approach for clarity estimate, while it needs picking out the edges manually. Due to the limits in accuracy, consistent or automation, these existing methods are not applicable to quality evaluation of optical remotesensing images. In this article, a new clarity-evaluation method, which is based on the principle of edge acutance algorithm, is proposed. In the new method, edge detection algorithm and gradient search algorithm are adopted to automatically search the object edges in images. Moreover, The calculation algorithm for edge sharpness has been improved. The new method has been tested with several groups of optical remotesensing images. Compared with the existing automatic evaluation methods, the new method perform better both in accuracy and consistency. Thus, the new method is an effective clarity evaluation method for optical remotesensing images.

The Laboratory of Environmental Applications of Lasers at IPEN realizes a study about atmospherics properties, such as extinction and backscattering coefficient. These coefficient are estimated by an inverse method, whose estimate quality is difficult to measure. This work presents a method with good statistic approach to retrieval the same coefficients. The new method, however, offers a number of advantages compared to the first method in use, including (1) the ability to incorporate different kinds of information under a common retrieval philosophy and (2) the method provides number of ways for evaluating the quality of the retrieval. Thus we hope improve the accuracy of estimates. (author)

the drawback of expensive conventional surveying methods. An airborne remotesensing system used for monitoring and surveillance of oil comprises different sensors such as side-looking airborne radar, synthetic aperture radar, infrared/ultraviolet line scanner...

The invention as disclosed is a non-contact method and apparatus for continuously monitoring a physiological event in a human or animal, such as blood pressure, which involves utilizing a laser-based...

Evapotranspiration (ET) is the largest term after precipitation in terrestrial water budgets. Accurate estimates of ET are needed for numerous agricultural and natural resource management tasks and to project changes in hydrological cycles due to potential climate change. We explore recent methods that combine vegetation indices (VI) from satellites with ground measurements of actual ET (ETa) and meteorological data to project ETa over a wide range of biome types and scales of measurement, from local to global estimates. The majority of these use time-series imagery from the Moderate Resolution Imaging Spectrometer on the Terra satellite to project ET over seasons and years. The review explores the theoretical basis for the methods, the types of ancillary data needed, and their accuracy and limitations. Coefficients of determination between modeled ETa and measured ETa are in the range of 0.45–0.95, and root mean square errors are in the range of 10–30% of mean ETa values across biomes, similar to methods that use thermal infrared bands to estimate ETa and within the range of accuracy of the ground measurements by which they are calibrated or validated. The advent of frequent-return satellites such as Terra and planed replacement platforms, and the increasing number of moisture and carbon flux tower sites over the globe, have made these methods feasible. Examples of operational algorithms for ET in agricultural and natural ecosystems are presented. The goal of the review is to enable potential end-users from different disciplines to adapt these methods to new applications that require spatially-distributed ET estimates.

Full Text Available Considering the classification of high spatial resolution remotesensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remotesensing imagery classification.

Following a comprehensive literature review, this paper looks at analysis of geohazard using remotesensing information. This paper compares the basic types and methods of change detection, explores the basic principle of common methods and makes an respective analysis of the characteristics and shortcomings of the commonly used methods in the application of geohazard. Using the earthquake in JieGu as a case study, this paper proposes a geohazard change detection method integrating RS and GIS. When detecting the pre-earthquake and post-earthquake remotesensing images at different phases, it is crucial to set an appropriate threshold. The method adopts a self-adapting determination algorithm for threshold. We select a training region which is obtained after pixel information comparison and set a threshold value. The threshold value separates the changed pixel maximum. Then we apply the threshold value to the entire image, which could also make change detection accuracy maximum. Finally, we output the result to the GIS system to make change analysis. The experimental results show that this method of geohazard change detection based on integrating remotesensing and GIS information has higher accuracy with obvious advantages compared with the traditional methods

RemoteSensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

..., and hyperspectral data processing. While there are many resources that suitably cover these areas individually and focus on specific aspects of the hyperspectral remotesensing field, this book provides a holistic treatment...

This book provides up-to-date developments, methods, and techniques in the field of GIS and remotesensing and features articles from internationally renowned authorities on three interrelated perspectives of scaling issues: scale in land surface properties, land surface patterns, and land surface processes. The book is ideal as a professional reference for practicing geographic information scientists and remotesensing engineers as well as a supplemental reading for graduate level students.

The purpose of the Workshop was to study in depth the application of remotesensing technology to the fields of archaeology, astronomy, geography, geology, and physics. Some emphasis was placed on utilizing remotesensingmethods and techniques in the search for water, mineral and land resources. The Workshop was attended by 90 people from 35 countries. The proceedings of this meeting includes 15 papers, 12 of them have a separate abstract in the INIS Database. Refs, figs and tabs

In order to increase the accuracy of cloud detection for remotesensing satellite imagery, we propose an efficient cloud detection method for remotesensing satellite panchromatic images. This method includes three main steps. First, an adaptive intensity threshold value combined with a median filter is adopted to extract the coarse cloud regions. Second, a guided filtering process is conducted to strengthen the textural features difference and then we conduct the detection process of texture via gray-level co-occurrence matrix based on the acquired texture detail image. Finally, the candidate cloud regions are extracted by the intersection of two coarse cloud regions above and we further adopt an adaptive morphological dilation to refine them for thin clouds in boundaries. The experimental results demonstrate the effectiveness of the proposed method.

This paper discusses using a remotesensing fusion method, based on' adaptive sparse representation (ASP)', to provide improved spectral information, reduce data redundancy and decrease system complexity. First, the training sample set is formed by taking random blocks from the images to be fused, the dictionary is then constructed using the training samples, and the remaining terms are clustered to obtain the complete dictionary by iterated processing at each step. Second, the self-adaptive weighted coefficient rule of regional energy is used to select the feature fusion coefficients and complete the reconstruction of the image blocks. Finally, the reconstructed image blocks are rearranged and an average is taken to obtain the final fused images. Experimental results show that the proposed method is superior to other traditional remotesensing image fusion methods in both spectral information preservation and spatial resolution.

The Mississippi Sound RemoteSensing Study was initiated as part of the research program of the NASA Earth Resources Laboratory. The objective of this study is development of remotesensing techniques to study near-shore marine waters. Included within this general objective are the following: (1) evaluate existing techniques and instruments used for remote measurement of parameters of interest within these waters; (2) develop methods for interpretation of state-of-the-art remotesensing data which are most meaningful to an understanding of processes taking place within near-shore waters; (3) define hardware development requirements and/or system specifications; (4) develop a system combining data from remote and surface measurements which will most efficiently assess conditions in near-shore waters; (5) conduct projects in coordination with appropriate operating agencies to demonstrate applicability of this research to environmental and economic problems.

Full Text Available Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD methods have been developed to solve them by utilizing remotesensing (RS images. The advent of high resolution (HR remotesensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remotesensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC segmentation. Then, saliency and morphological building index (MBI extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF. Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

The objectives of this research are: to develop and test predictive relations for the quantitative influence of vegetation canopy structure on wind erosion of semiarid rangeland soils, and to develop remotesensingmethods for measuring the canopy structural parameters that determine sheltering against wind erosion. The influence of canopy structure on wind erosion will be investigated by means of wind-tunnel and field experiments using structural variables identified by the wind-tunnel and field experiments using model roughness elements to simulate plant canopies. The canopy structural variables identified by the wind-tunnel and field experiments as important in determining vegetative sheltering against wind erosion will then be measured at a number of naturally vegetated field sites and compared with estimates of these variables derived from analysis of remotelysensed data.

The MULINEM (The Medieval Urban Landscape in Northeastern Mesopotamia) project is aiming to investigate a Late Sasanian and Islamic urban network in the land of Erbil, historic province of Hidyab (Adiabene) that is located in the northern Iraq. The research of the hierarchical urban network in a defined area belongs to approaches rarely used in the study of the Islamic urbanism. The project focuses on the cluster of urban sites of the 6th-17th centuries A.D. This paper focuses on remotesensing analysis of historical sites with special interest of FORMOSAT-2 data that have been gained through a research announcement: Free FORMOSAT-2 satellite Imagery. Documentation of two archaeological sites (Makhmúr al-Qadima and Kushaf) are introduced. FORMOSAT-2 data results have been compared to historic CORONA satellite data of mentioned historical sites purchased earlier by the University of West Bohemia. Remotesensingmethods were completed using in-situ measurements.

To improve the efficiency of stereo information for remotesensing classification, a stereo remotesensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remotesensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

At present, the extraction of earthquake disaster information from remotesensing data relies on visual interpretation. However, this technique cannot effectively and quickly obtain precise and efficient information for earthquake relief and emergency management. Collapsed buildings in the town of Zipingpu after the Wenchuan earthquake were used as a case study to validate two kinds of rapid extraction methods for earthquake-collapsed building information based on pixel-oriented and object-oriented theories. The pixel-oriented method is based on multi-layer regional segments that embody the core layers and segments of the object-oriented method. The key idea is to mask layer by layer all image information, including that on the collapsed buildings. Compared with traditional techniques, the pixel-oriented method is innovative because it allows considerably rapid computer processing. As for the object-oriented method, a multi-scale segment algorithm was applied to build a three-layer hierarchy. By analyzing the spectrum, texture, shape, location, and context of individual object classes in different layers, the fuzzy determined rule system was established for the extraction of earthquake-collapsed building information. We compared the two sets of results using three variables: precision assessment, visual effect, and principle. Both methods can extract earthquake-collapsed building information quickly and accurately. The object-oriented method successfully overcomes the pepper salt noise caused by the spectral diversity of high-resolution remotesensing data and solves the problem of same object, different spectrums and that of same spectrum, different objects. With an overall accuracy of 90.38%, the method achieves more scientific and accurate results compared with the pixel-oriented method (76.84%). The object-oriented image analysis method can be extensively applied in the extraction of earthquake disaster information based on high-resolution remotesensing

This presentation is part of the Independent Science Board of the State of California Delta Stewardship Council brown bag seminar series on the "How the Delta is Monitored", followed with a panel discussion. Various remotesensing approaches for aquatic vegetation will be reviewed. Key research and application issues with remotesensing monitoring in the Delta will be addressed.

A leading text for undergraduate- and graduate-level courses, this book introduces widely used forms of remotesensing imagery and their applications in plant sciences, hydrology, earth sciences, and land use analysis. The text provides comprehensive coverage of principal topics and serves as a framework for organizing the vast amount of remotesensing information available on the Web. Including case studies and review questions, the book's four sections and 21 chapters are carefully designed as independent units that instructors can select from as needed for their courses. Illustrations in

This lecture was just a taste of radar remotesensing techniques and applications. Other important areas include Stereo radar grammetry. PolInSAR for volumetric structure mapping. Agricultural monitoring, soil moisture, ice-mapping, etc. The broad range of sensor types, frequencies of observation and availability of sensors have enabled radar sensors to make significant contributions in a wide area of earth and planetary remotesensing sciences. The range of applications, both qualitative and quantitative, continue to expand with each new generation of sensors.

Full Text Available Spatial correlation between pixels is important information for remotelysensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary’s C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM for the classification of multi-features (e.g. the spectral feature and spatial correlation feature. In order to validate the effectiveness of the developed method, experiments are conducted on different remotelysensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.

Surveillance and tracking of oil spills has been a feature of most spill response situations for many years. The simplest and most direct method uses visual observations from an aircraft and hand-plotting of the data on a map. This technique has proven adequate for most small spills and for responses in fair weather. As the size of the spill increases or the weather deteriorates, there is a need to augment visual aerial observations with remotesensingmethods. Remotesensing and its associated systems are one of the most technically complex and sophisticated elements of an oil spill response. During the past few years, a number of initiatives have been undertaken to use contemporary electronic and computing systems to develop new and improved remotesensing systems

This paper presents remotesensing best practice in the wind industry. Remotesensing is a technique whereby measurements are obtained from the interaction of laser or acoustic pulses with the atmosphere. There is a vast diversity of tools and techniques available and they offer wide scope for reducing project uncertainty and risk but best practice must take into account versatility and flexibility. It should focus on the outcome in terms of results and data. However, traceability of accuracy requires comparison with conventional instruments. The framework for the Boulder protocol is given. Overviews of the guidelines for IEA SODAR and IEA LIDAR are also mentioned. The important elements of IEC 61400-12-1, an international standard for wind turbines, are given. Bankability is defined based on the Boulder protocol and a pie chart is presented that illustrates the uncertainty area covered by remotesensing. In conclusion it can be said that remotesensing is changing perceptions about how wind energy assessments can be made.

Remotesensing is an important data source for monitoring the change of forest cover, in terms of both total removal of forest cover (deforestation), and change of canopy cover, structure and forest ecosystem services that result in forest degradation. In the context of Intergovernmental Panel on Climate Change (IPCC), forest degradation monitoring requires information...

Full Text Available Soil salinity is an important factor that affects plant growth and reduces production of plantat different growth stages Remotesensing technology and GIS have a great potential for monitoring dynamic soil processes such as salinity. In the present study the efficiency of remotesensing technology and its integration with GIS was examined to estimate soil salinity for Neyshabour basin. Different classification methods for soil salinity were also investigated. We used 6 bands of LandSat ETM+ for this study. Classification results obtained from applying mathematical models for the images were compared with different band combinations results. The area of saline and non saline soil classes were identified in the study area based on the both methods and also based on the combination of the two methods. The results showed that the best method for soil classification was using of the two methods in the first stage to separate two classes of saline and non saline soils and then classifying the non saline soils in the second stage. As the variation in the numerical values of the image for different soil salinity in the study area was small, it was concluded that there is a limit potential of LandSat ETM+ images for identifying and classification of soil salinity in such an area.

Full Text Available This paper presents a new classification method for high-spatial-resolution remotesensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine. Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remotesensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy.

Full Text Available Road extraction from high-resolution remotesensing image is an important and difficult task.Since remotesensing images include complicated information,the methods that extract roads by spectral,texture and linear features have certain limitations.Also,many methods need human-intervention to get the road seeds(semi-automatic extraction,which have the great human-dependence and low efficiency.The road-extraction method,which uses the image segmentation based on principle of local gray consistency and integration shape features,is proposed in this paper.Firstly,the image is segmented,and then the linear and curve roads are obtained by using several object shape features,so the method that just only extract linear roads are rectified.Secondly,the step of road extraction is carried out based on the region growth,the road seeds are automatic selected and the road network is extracted.Finally,the extracted roads are regulated by combining the edge information.In experiments,the images that including the better gray uniform of road and the worse illuminated of road surface were chosen,and the results prove that the method of this study is promising.

The application of remotesensing to the study of lakes is begun in years 80 with the lunch of the satellites of second generation. Many experiences have indicated the contribution of remotesensing for the limnology [it

Contents: Remotesensing of wind shear and the theory and development of acoustic doppler; Wind studies; A comparison of methods for the remote detection of winds in the airport environment; Acoustic doppler system development; System calibration; Airport operational tests.

In this study, we present the main features of the information operator approach for solving linear inverse problems arising in atmospheric remotesensing. This method is superior to the stochastic version of the Tikhonov regularization (or the optimal estimation method) due to its capability to filter out the noise-dominated components of the solution generated by an inappropriate choice of the regularization parameter. We extend this approach to iterative methods for nonlinear ill-posed problems and derive the truncated versions of the Gauss-Newton and Levenberg-Marquardt methods. Although the paper mostly focuses on discussing the mathematical details of the inverse method, retrieval results have been provided, which exemplify the performances of the methods. These results correspond to the NO 2 retrieval from SCIAMACHY limb scatter measurements and have been obtained by using the retrieval processors developed at the German Aerospace Center Oberpfaffenhofen and Institute of Environmental Physics of the University of Bremen

Full Text Available Subject of Research. Research findings of the specific application of space-based optical-electronic and radar means for the Earth remotesensing are considered. The subject matter of the study is the current planning of objects survey on the underlying surface in order to increase the effectiveness of sensing system due to the rational use of its resources. Method. New concept of a group object, stochastic swath and stochastic length of the route is introduced. The overview of models for single, group objects and their parameters is given. The criterion for the existence of the group object based on two single objects is formulated. The method for group objects formation while current survey planning has been developed and its description is presented. The method comprises several processing stages for data about objects with the calculation of new parameters, the stochastic characteristics of space means and validates the spatial size of the object value of the stochastic swath and stochastic length of the route. The strict mathematical description of techniques for model creation of a group object based on data about a single object and onboard special complex facilities in difficult conditions of registration of spatial data is given. Main Results. The developed method is implemented on the basis of modern geographic information system in the form of a software tool layout with advanced tools of processing and analysis of spatial data in vector format. Experimental studies of the forming method for the group of objects were carried out on a different real object environment using the parameters of modern national systems of the Earth remotesensing detailed observation Canopus-B and Resurs-P. Practical Relevance. The proposed models and method are focused on practical implementation using vector spatial data models and modern geoinformation technologies. Practical value lies in the reduction in the amount of consumable resources by means of

Actual evaporation (Eta) is an essential variable to assess water availability, drought risk and food security, among others. Measurements of Eta are however limited to a small footprint, hampering a spatially explicit analysis and application and are very often not available at all. To overcome the problem of data scarcity, Eta can be assessed by various remotesensing approaches such as the Triangle Method (Jiang & Islam, 1999). Here, Eta is estimated by using the Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST). In this study, the R-package 'TriangleMethod' was compiled to efficiently perform the calculations of NDVI and processing LST to finally derive Eta from the applied data set. The package contains all necessary calculation steps and allows easy processing of a large data base of remotesensing images. By default, the parameterization for the Landsat TM and ETM+ sensors are implemented, however, the algorithms can be easily extended to additional sensors. The auxiliary variables required to estimate Eta with this method, such as elevation, solar radiation and air temperature at the overpassing time, can be processed as gridded information to allow for a better representation of the study area. The package was successfully applied in various studies in Spain, Palestine, Costa Rica and Canada.

At present, in the inspection and acceptance of high spatial resolution remotlysensed orthophoto image, the horizontal accuracy detection is testing and evaluating the accuracy of images, which mostly based on a set of testing points with the same accuracy and reliability. However, it is difficult to get a set of testing points with the same accuracy and reliability in the areas where the field measurement is difficult and the reference data with high accuracy is not enough. So it is difficult to test and evaluate the horizontal accuracy of the orthophoto image. The uncertainty of the horizontal accuracy has become a bottleneck for the application of satellite borne high-resolution remotesensing image and the scope of service expansion. Therefore, this paper proposes a new method to test the horizontal accuracy of orthophoto image. This method using the testing points with different accuracy and reliability. These points' source is high accuracy reference data and field measurement. The new method solves the horizontal accuracy detection of the orthophoto image in the difficult areas and provides the basis for providing reliable orthophoto images to the users.

Thermal Infrared (TIR) remotesensing data can provide important measurements of surface energy fluxes and temperatures, which are integral to understanding landscape processes and responses. One example of this is the successful application of TIR remotesensing data to estimate evapotranspiration and soil moisture, where results from a number of studies suggest that satellite-based measurements from TIR remotesensing data can lead to more accurate regional-scale estimates of daily evapotranspiration. With further refinement in analytical techniques and models, the use of TIR data from airborne and satellite sensors could be very useful for parameterizing surface moisture conditions and developing better simulations of landscape energy exchange over a variety of conditions and space and time scales. Thus, TIR remotesensing data can significantly contribute to the observation, measurement, and analysis of energy balance characteristics (i.e., the fluxes and redistribution of thermal energy within and across the land surface) as an implicit and important aspect of landscape dynamics and landscape functioning. The application of TIR remotesensing data in landscape ecological studies has been limited, however, for several fundamental reasons that relate primarily to the perceived difficulty in use and availability of these data by the landscape ecology community, and from the fragmentation of references on TIR remotesensing throughout the scientific literature. It is our purpose here to provide evidence from work that has employed TIR remotesensing for analysis of landscape characteristics to illustrate how these data can provide important data for the improved measurement of landscape energy response and energy flux relationships. We examine the direct or indirect use of TIR remotesensing data to analyze landscape biophysical characteristics, thereby offering some insight on how these data can be used more robustly to further the understanding and modeling of

Remotesensing to monitor the behaviour of terrestrial ecosystems over large areas was discussed. For this type of application the boreal ecosystem productivity simulator (BEPS) was developed, with the subsequent incorporation of the more advanced photosynthetic model. The new model improves the methodology through analytical spatial and temporal integration of canopy photosynthesis processes, and is suitable for regional remotesensing applications at moderate resolutions of 250 to 1000 m. 10 refs., 1 tab., 3 figs.

Highlights: • We demonstrate adjoint methods for atmospheric remotesensing in a two-dimensional setting. • Searchlight functions are used to handle the singularity of measurement response functions. • Adjoint methods require two radiative transfer calculations to evaluate the measurement misfit function and its derivatives with respect to all unknown parameters. • Synthetic retrieval studies show the scalability of adjoint methods to problems with thousands of measurements and unknown parameters. • Adjoint methods and the searchlight function technique are generalizable to 3D remotesensing. - Abstract: In previous work, we derived the adjoint method as a computationally efficient path to three-dimensional (3D) retrievals of clouds and aerosols. In this paper we will demonstrate the use of adjoint methods for retrieving two-dimensional (2D) fields of cloud extinction. The demonstration uses a new 2D radiative transfer solver (FSDOM). This radiation code was augmented with adjoint methods to allow efficient derivative calculations needed to retrieve cloud and surface properties from multi-angle reflectance measurements. The code was then used in three synthetic retrieval studies. Our retrieval algorithm adjusts the cloud extinction field and surface albedo to minimize the measurement misfit function with a gradient-based, quasi-Newton approach. At each step we compute the value of the misfit function and its gradient with two calls to the solver FSDOM. First we solve the forward radiative transfer equation to compute the residual misfit with measurements, and second we solve the adjoint radiative transfer equation to compute the gradient of the misfit function with respect to all unknowns. The synthetic retrieval studies verify that adjoint methods are scalable to retrieval problems with many measurements and unknowns. We can retrieve the vertically-integrated optical depth of moderately thick clouds as a function of the horizontal coordinate. It is also

Serious land desertification and sandified threaten the urban ecological security and the sustainable economic and social development. In recent years, a large number of mobile sand dunes in Horqin sandy land flow into the northwest of Liaoning Province under the monsoon, make local agriculture suffer serious harm. According to the characteristics of desertification land in northwestern Liaoning, based on the First National Geographical Survey data, the Second National Land Survey data and the 1984-2014 Landsat satellite long time sequence data and other multi-source data, we constructed a remotesensing monitoring index system of desertification land in Northwest Liaoning. Through the analysis of space-time-spectral characteristics of desertification land, a method for multi-spectral remotesensing image recognition of desertification land under time-space constraints is proposed. This method was used to identify and extract the distribution and classification of desertification land of Chaoyang City (a typical citie of desertification in northwestern Liaoning) in 2008 and 2014, and monitored the changes and transfers of desertification land from 2008 to 2014. Sandification information was added to the analysis of traditional landscape changes, improved the analysis model of desertification land landscape index, and the characteristics and laws of landscape dynamics and landscape pattern change of desertification land from 2008 to 2014 were analyzed and revealed.

In a large number of scenarios and missions, the technical, operational and economical advantages of UAS-based photogrammetry and remotesensing over traditional airborne and satellite platforms are apparent. Airborne Synthetic Aperture Radar (SAR) or combined optical/SAR operation in remote areas might be a case of a typical "dull, dirty, dangerous" mission suitable for unmanned operation - in harsh environments such as for example rain forest areas in Brazil, topographic mapping of small to medium sparsely inhabited remote areas with UAS-based photogrammetry and remotesensing seems to be a reasonable paradigm. An example of such a system is the SARVANT platform, a fixed-wing aerial vehicle with a six-meter wingspan and a maximumtake- of-weight of 140 kilograms, able to carry a fifty-kilogram payload. SARVANT includes a multi-band (X and P) interferometric SAR payload, as the P-band enables the topographic mapping of densely tree-covered areas, providing terrain profile information. Moreover, the combination of X- and P-band measurements can be used to extract biomass estimations. Finally, long-term plan entails to incorporate surveying capabilities also at optical bands and deliver real-time imagery to a control station. This paper focuses on the remote-sensing concept in SARVANT, composed by the aforementioned SAR sensor and envisioning a double optical camera configuration to cover the visible and the near-infrared spectrum. The flexibility on the optical payload election, ranging from professional, medium-format cameras to mass-market, small-format cameras, is discussed as a driver in the SARVANT development. The paper also focuses on the navigation and orientation payloads, including the sensors (IMU and GNSS), the measurement acquisition system and the proposed navigation and orientation methods. The latter includes the Fast AT procedure, which performs close to traditional Integrated Sensor Orientation (ISO) and better than Direct Sensor Orientation (Di

Description of data devices for deriving multi-spectral measuring television measurement data of middle and high resolution through use of second generation Meteor-type satellites. Options for developing a permanent and active remotesensing system in USSR are discussed. It is noted that the present experiment is an important step in that direction. Design and structural data for this particular device and its application in the experiment are covered.

Quantitative estimation of vegetation water content(VWC) using optical remotesensing techniques is helpful in forest fire as-sessment,agricultural drought monitoring and crop yield estimation.This paper reviews the research advances of VWC retrieval using spectral reflectance,spectral water index and radiative transfer model(RTM) methods.It also evaluates the reli-ability of VWC estimation using spectral water index from the observation data and the RTM.Focusing on two main definitions of VWC-the fuel moisture content(FMC) and the equivalent water thickness(EWT),the retrieval accuracies of FMC and EWT using vegetation water indices are analyzed.Moreover,the measured information and the dataset are used to estimate VWC,the results show there are significant correlations among three kinds of vegetation water indices(i.e.,WSI,NDⅡ,NDWI1640,WI/NDVI) and canopy FMC of winter wheat(n=45).Finally,the future development directions of VWC detection based on optical remotesensing techniques are also summarized.

Full Text Available There has always been a need to directly perceive and study the events whose extent is beyond people's possibilities. In order to get new data and to make observations and studying much more objective in comparison with past syntheses - a new method of examination called remotesensing has been adopted. The paper deals with the principles and elements of remotesensing, as well as with the basic aspects of using remote research in examining meteorological (weather parameters and the conditions of the atmosphere. The usage of satellite images is possible in all phases of the global and systematic research of different natural phenomena when airplane and satellite images of different characteristics are used and their analysis and interpretation is carried out by viewing and computer added procedures. Introduction Remotesensing of the Earth enables observing and studying global and local events that occur on it. Satellite images are nowadays used in geology, agriculture, forestry, geodesy, meteorology, spatial and urbanism planning, designing of infrastructure and other objects, protection from natural and technological catastrophes, etc. It it possible to use satellite images in all phases of global and systematic research of different natural phenomena. Basics of remotesensingRemotesensing is a method of the acquisition and interpretation of information about remote objects without making a physical contact with them. The term Daljinska detekcija is a literal translation of the English term RemoteSensing. In French it isTeledetection, in German - Fernerkundung, in Russian - дистанционие иследования. We also use terms such as: remote survailance, remote research, teledetection, remotemethods, and distance research. The basic elements included in RemoteSensing are: object, electromagnetic energy, sensor, platform, image, analysis, interpretation and the information (data, fact. Usage of satellite remote research in

Taking Yili Basin as an example, remotesensing technology and method of interlayer oxidation zone type sandstone uranium deposit have systematically been summarized. Firstly, principle, methods and procedures of the second development of scientific experimental satellite photograph have been elaborated in detail. Three dimensional stereo simulation, display, and multi-parameters extraction have been recommended. Secondarily, the research is focused on prospective section image features in different type images and their geological implications and on establishing recognition keys of promising areas. Finally, based on above research results, three graded predictions, i.e. regional prospect, promising sections and favourable location in the deposit have been made step by step and reconnaissance and prospecting range are gradually reduced. The practice has indicated that breakthrough progress has been made in application to prospect prognosis of interlayer oxidation zone type sandstone uranium deposit and good verified results have been obtained

The mass balance of the Greenland Ice Sheet is virtually impossible to obtain with traditional ground-based methods alone due to its vast size. It is thus desirable to develop mass-balance methods depending on remotesensing instead and this field has experienced a dramatic development within...... of measured surface elevation change over a 50x50~km part of the western Greenland Ice-Sheet margin near Kangerlussuaq. In this region, the mean observed elevation change has been -0.5~m from 2000 to 2003. However, the change is unevenly distributed with the northern and central part generally in balance...... the last decade. Large amounts of data have been collected from satellite and airborne platforms, yielding surface elevation changes and surface velocity fields. Here we present data from the Greenland Ice-Sheet margin acquired with a new small-scale airborne system, designed for regional high...

Full Text Available Modern geomorphologic investigations of condition and change of the intensity of erosive process should be based on application of remotesensingmethods which are based on processing of aerial and satellite photographs. Using of these methods is very important because it enables good possibilities for realizing regional relations of the investigated phenomenon, as well as the estimate of spatial and temporal variability of all physical-geographical and anthropogenic factors influencing given process. Realizing process of land erosion, on the whole, is only possible by creating universal data base, as well as by using of appropriate software, more exactly by establishing uniform information system. Geographical information system, as the most effective one, the most complex and the most integral system of information about the space enables unification as well as analytical and synthetically processing of all data.

Sensing systems are an important element of mobile teleoperators and robots. This paper discusses certain problems and limitations of vision and other sensing systems with respect to operations in a radiological accident environment. Methods which appear promising for near-term improvements to sensor technology are described. 3 refs

This document is designed to help senior high school students study remotesensing technology and techniques in relation to the environmental sciences. It discusses the acquisition, analysis, and use of ecological remote data. Material is divided into three sections and an appendix. Section One is an overview of the basics of remotesensing.…

Time-series remotesensing images record changes happening on the earth surface, which include not only abnormal changes like human activities and emergencies (e.g. fire, drought, insect pest etc.), but also changes caused by vegetation phenology and climate changes. Yet, challenges occur in analyzing global environment changes and even the internal forces. This paper proposes a robust Anomaly Based Change Detection method (ABCD) for time-series images analysis by detecting abnormal points in data sets, which do not need to follow a normal distribution. With ABCD we can detect when and where changes occur, which is the prerequisite condition of global change studies. ABCD was tested initially with 10-day SPOT VGT NDVI (Normalized Difference Vegetation Index) times series tracking land cover type changes, seasonality and noise, then validated to real data in a large area in Jiangxi, south of China. Initial results show that ABCD can precisely detect spatial and temporal changes from long time series images rapidly.

Many undocumented and commonly unmaintained levees exist in the landscape complicating flood forecasting, risk management, and emergency response. This report describes a pilot study completed by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers to assess two methods to identify undocumented levees by using remotelysensed, high-resolution topographic data. For the first method, the U.S. Army Corps of Engineers examined hillshades computed from a digital elevation model that was derived from light detection and ranging (lidar) to visually identify potential levees and then used detailed site visits to assess the validity of the identifications. For the second method, the U.S. Geological Survey applied a wavelet transform to a lidar-derived digital elevation model to identify potential levees. The hillshade method was applied to Delano, Minnesota, and the wavelet-transform method was applied to Delano and Springfield, Minnesota. Both methods were successful in identifying levees but also identified other features that required interpretation to differentiate from levees such as constructed barriers, high banks, and bluffs. Both methods are complementary to each other, and a potential conjunctive method for testing in the future includes (1) use of the wavelet-transform method to rapidly identify slope-break features in high-resolution topographic data, (2) further examination of topographic data using hillshades and aerial photographs to classify features and map potential levees, and (3) a verification check of each identified potential levee with local officials and field visits.

The segmentation of a high spatial resolution remotesensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods.

Full Text Available The segmentation of a high spatial resolution remotesensing image is a critical step in geographic object-based image analysis (GEOBIA. Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods.

Support Vector Machine (SVM) has been proved to be suitable for classification of remotesensing image and proposed to overcome the Hughes phenomenon. Hyper-spectral sensors are intrinsically designed to discriminate among a broad range of land cover classes which may lead to high computational time in SVM mutil-class algorithms. Model selection for SVM involving kernel and the margin parameter values selection which is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyper-spectral remotesensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, particle swarm algorithm is introduced to the optimal selection of SVM (PSSVM) kernel parameter σ and margin parameter C to improve the modelling efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for evaluating the novel PSSVM, as well as traditional SVM classifier with general Grid-Search cross-validation method (GSSVM). And then, evaluation indexes including SVM model training time, classification Overall Accuracy (OA) and Kappa index of both PSSVM and GSSVM are all analyzed quantitatively. It is demonstrated that OA of PSSVM on test samples and whole image are 85% and 82%, the differences with that of GSSVM are both within 0.08% respectively. And Kappa indexes reach 0.82 and 0.77, the differences with that of GSSVM are both within 0.001. While the modelling time of PSSVM can be only 1/10 of that of GSSVM, and the modelling. Therefore, PSSVM is an fast and accurate algorithm for hyper-spectral image classification and is superior to GSSVM

The RemoteSensing in Wind Energy report provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind it began in year 2008 at DTU Wind Energy (formerly Risø) during the first PhD Summer School: RemoteSensing in Wind Energy...... state-of-the-art ‘guideline’ available for people involved in RemoteSensing in Wind Energy....

The RemoteSensing in Wind Energy Compendium provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind this compendium began in year 2008 at Risø DTU during the first PhD Summer School: RemoteSensing in Wind Energy. Thus......-of-the-art compendium available for people involved in RemoteSensing in Wind Energy....

Geology is defined as the 'study of the planet Earth - the materials of which it is made, the processes that act on these materials, the products formed, and the history of the planet and its life forms since its origin' (Bates and Jackson, 1976). Remotesensing has seen a number of variable definitions such as those by Sabins and Lillesand and Kiefer in their respective textbooks (Sabins, 1996; Lillesand and Kiefer, 2000). Floyd Sabins (Sabins, 1996) defined it as 'the science of acquiring, processing and interpreting images that record the interaction between electromagnetic energy and matter' while Lillesand and Kiefer (Lillesand and Kiefer, 2000) defined it as 'the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation'. Thus Geological RemoteSensing can be considered the study of, not just Earth given the breadth of work undertaken in planetary science, geological features and surfaces and their interaction with the electromagnetic spectrum using technology that is not in direct contact with the features of interest.

The rapid increase in digital data volumes from new and existing sensors necessitates the need for efficient analytical tools for extracting information. We developed an integrated software package called ICAMS (Image Characterization and Modeling System) to provide specialized spatial analytical functions for interpreting remotesensing data. This paper evaluates the three fractal dimension measurement methods: isarithm, variogram, and triangular prism, along with the spatial autocorrelation measurement methods Moran's I and Geary's C, that have been implemented in ICAMS. A modified triangular prism method was proposed and implemented. Results from analyzing 25 simulated surfaces having known fractal dimensions show that both the isarithm and triangular prism methods can accurately measure a range of fractal surfaces. The triangular prism method is most accurate at estimating the fractal dimension of higher spatial complexity, but it is sensitive to contrast stretching. The variogram method is a comparatively poor estimator for all of the surfaces, particularly those with higher fractal dimensions. Similar to the fractal techniques, the spatial autocorrelation techniques are found to be useful to measure complex images but not images with low dimensionality. These fractal measurement methods can be applied directly to unclassified images and could serve as a tool for change detection and data mining.

Subsurface remotesensing measurements are widely used for oil and gas exploration, for oil and gas production monitoring, and for basic studies in the earth sciences. Radiation sensors, often including small accelerator sources, are used to obtain bulk properties of the surrounding strata as well as to provide detailed elemental analyses of the rocks and fluids in rock pores. Typically, instrument packages are lowered into a borehole at the end of a long cable, that may be as long as 10 km, and two-way data and instruction telemetry allows a single radiation instrument to operate in different modes and to send the data to a surface computer. Because these boreholes are often in remote locations throughout the world, the data are frequently transmitted by satellite to various locations around the world for almost real-time analysis and incorporation with other data. The complete system approach that permits rapid and reliable data acquisition, remote analysis and transmission to those making decisions is described

Estimation of total leaf area (LAT) is important to express biochemical properties in plant ecology and remotesensing studies. A measurement of LAT is easy in broadleaf species, but it remains challenging in coniferous canopies. We proposed a new geometrical model to estimate Norway spruce LAT and

Full Text Available Traditionally, to map environmental features using remotesensing, practitioners will use training data to develop models on various satellite data sets using a number of classification approaches and use test data to select a single ‘best performer...

Evapotranspiration (ET) is an important hydrological process that can be studied and estimated at multiple spatial scales ranging from a leaf to a river basin. We present a review of methods in estimating basin scale ET and its applications in understanding basin water balance dynamics. The review focuses on two aspects of ET: (i) how the basin scale water balance approach is used to estimate ET; and (ii) how ‘direct’ measurement and modelling approaches are used to estimate basin scale ET. Obviously, the basin water balance-based ET requires the availability of good precipitation and discharge data to calculate ET as a residual on longer time scales (annual) where net storage changes are assumed to be negligible. ET estimated from such a basin water balance principle is generally used for validating the performance of ET models. On the other hand, many of the direct estimation methods involve the use of remotelysensed data to estimate spatially explicit ET and use basin-wide averaging to estimate basin scale ET. The direct methods can be grouped into soil moisture balance modelling, satellite-based vegetation index methods, and methods based on satellite land surface temperature measurements that convert potential ET into actual ET using a proportionality relationship. The review also includes the use of complementary ET estimation principles for large area applications. The review identifies the need to compare and evaluate the different ET approaches using standard data sets in basins covering different hydro-climatic regions of the world.

Full Text Available Remotesensing (RS image segmentation is an essential step in geographic object-based image analysis (GEOBIA to ultimately derive “meaningful objects”. While many segmentation methods exist, most of them are not efficient for large data sets. Thus, the goal of this research is to develop an efficient parallel multi-scale segmentation method for RS imagery by combining graph theory and the fractal net evolution approach (FNEA. Specifically, a minimum spanning tree (MST algorithm in graph theory is proposed to be combined with a minimum heterogeneity rule (MHR algorithm that is used in FNEA. The MST algorithm is used for the initial segmentation while the MHR algorithm is used for object merging. An efficient implementation of the segmentation strategy is presented using data partition and the “reverse searching-forward processing” chain based on message passing interface (MPI parallel technology. Segmentation results of the proposed method using images from multiple sensors (airborne, SPECIM AISA EAGLE II, WorldView-2, RADARSAT-2 and different selected landscapes (residential/industrial, residential/agriculture covering four test sites indicated its efficiency in accuracy and speed. We conclude that the proposed method is applicable and efficient for the segmentation of a variety of RS imagery (airborne optical, satellite optical, SAR, high-spectral, while the accuracy is comparable with that of the FNEA method.

Advantages and disadvantages of modern discrete-ordinates finite-element methods for the solution of radiative transfer problems in meteorology, climatology, and remotesensing applications are evaluated. After the common basis of the formulation of radiative transfer problems in the fields of neutron transport and atmospheric optics is established, the essential features of the discrete-ordinates finite-element method are described including the limitations of the method and their remedies. Numerical results are presented for 1-D and 2-D atmospheric radiative transfer problems where integral as well as angular dependent quantities are compared with published results from other calculations and with measured data. These comparisons provide a verification of the discrete-ordinates results for a wide spectrum of cases with varying degrees of absorption, scattering, and anisotropic phase functions. Accuracy and computational speed are also discussed. Since practically all discrete-ordinates codes offer a builtin adjoint capability, the general concept of the adjoint method is described and illustrated by sample problems. Our general conclusion is that the strengths of the discrete-ordinates finite-element method outweight its weaknesses. We demonstrate that existing general-purpose discrete-ordinates codes can provide a powerful tool to analyze radiative transfer problems through the atmosphere, especially when 2-D geometries must be considered

Full Text Available Fast technological developments in photogrammetry and remotesensing areas demand quick and steady changes in the education programme and its realization. The university teachers and assistants are faced with ensuring the learning materials, data and software for practical lessons, as well as project proposals for student’s team work and bachelor or master thesis. In this paper the emerging topics that already have a considerable impact in the practice are treated mostly from the educational aspect. These relatively new topics that are considered in this paper are unmanned aerial systems for spatial data collection, terrestrial and aerial laser scanning, mobile mapping systems, and novelties in satellite remotesensing. The focus is given to practical implementation of these topics into the teaching and learning programme of Geodesy and Geoinformation at the University of Ljubljana, Faculty of Civil and Geodetic Engineering, and experiences gained by the authors so far. Together with the technological advances, the teaching approaches must be modernized as well. Classical approaches of teaching, where a lecturer gives lecture ex cathedra and students are only listeners, are not effective enough. The didactics science of teaching has developed and proved in the practice many useful approaches that can better motivate students for more active learning. We can use different methods of team work like pro et contra debate, buzzing groups, press conference, moderated discussion etc. An experimental study on active teaching methods in the class of students of the Master programme of Geodesy and Geoinformation has been made and the results are presented. After using some new teaching methods in the class, the students were asked to answer two types of a questionnaire. First questionnaire was the standard form developed by Noel Entwistle, an educational psychologist who developed the Approaches to Studying Inventory (ASI for identifying deep and

Fast technological developments in photogrammetry and remotesensing areas demand quick and steady changes in the education programme and its realization. The university teachers and assistants are faced with ensuring the learning materials, data and software for practical lessons, as well as project proposals for student's team work and bachelor or master thesis. In this paper the emerging topics that already have a considerable impact in the practice are treated mostly from the educational aspect. These relatively new topics that are considered in this paper are unmanned aerial systems for spatial data collection, terrestrial and aerial laser scanning, mobile mapping systems, and novelties in satellite remotesensing. The focus is given to practical implementation of these topics into the teaching and learning programme of Geodesy and Geoinformation at the University of Ljubljana, Faculty of Civil and Geodetic Engineering, and experiences gained by the authors so far. Together with the technological advances, the teaching approaches must be modernized as well. Classical approaches of teaching, where a lecturer gives lecture ex cathedra and students are only listeners, are not effective enough. The didactics science of teaching has developed and proved in the practice many useful approaches that can better motivate students for more active learning. We can use different methods of team work like pro et contra debate, buzzing groups, press conference, moderated discussion etc. An experimental study on active teaching methods in the class of students of the Master programme of Geodesy and Geoinformation has been made and the results are presented. After using some new teaching methods in the class, the students were asked to answer two types of a questionnaire. First questionnaire was the standard form developed by Noel Entwistle, an educational psychologist who developed the Approaches to Studying Inventory (ASI) for identifying deep and surface approaches to

Full Text Available The paper deals with technologies of ground secondary processing of heterogeneous multispectral data. The factors of heterogeneous data include uneven illumination of objects on the Earth surface caused by different properties of the relief. A procedure for the image restoration of spectral channels by means of terrain distortion compensation is developed. The object matter of this paper is to improve the quality of the results during image restoration of areas with large and medium landforms. Methods. Researches are based on the elements of the digital image processing theory, statistical processing of the observation results and the theory of multi-dimensional arrays. Main Results. The author has introduced operations on multidimensional arrays: concatenation and elementwise division. Extended model description for input data about the area is given. The model contains all necessary data for image restoration. Correction method for multispectral data radiometric distortions of the Earth remotesensing has been developed. The method consists of two phases: construction of empirical dependences for spectral reflectance on the relief properties and restoration of spectral images according to semiempirical data. Practical Relevance. Research novelty lies in developme nt of the application theory of multidimensional arrays with respect to the processing of multispectral data, together with data on the topography and terrain objects. The results are usable for development of radiometric data correction tools. Processing is performed on the basis of a digital terrain model without carrying out ground works connected with research of the objects reflective properties.

Restoration of Very High Resolution (VHR) optical RemoteSensing Image (RSI) is critical and leads to the problem of removing instrumental noise while keeping integrity of relevant information. Improving denoising in an image processing chain implies increasing image quality and improving performance of all following tasks operated by experts (photo-interpretation, cartography, etc.) or by algorithms (land cover mapping, change detection, 3D reconstruction, etc.). In a context of large industrial VHR image production, the selected denoising method should optimized accuracy and robustness with relevant information and saliency conservation, and rapidity due to the huge amount of data acquired and/or archived. Very recent research in image processing leads to a fast and accurate algorithm called Non Local Bayes (NLB) that we propose to adapt and optimize for VHR RSIs. This method is well suited for mass production thanks to its best trade-off between accuracy and computational complexity compared to other state-of-the-art methods. NLB is based on a simple principle: similar structures in an image have similar noise distribution and thus can be denoised with the same noise estimation. In this paper, we describe in details algorithm operations and performances, and analyze parameter sensibilities on various typical real areas observed in VHR RSIs.

Taking Yingxiu, the epicentre of the Wenchuan earthquake, as the study area, a method for geological disaster extraction using high-resolution remotesensing imagery was proposed in this study. A high-resolution Digital Elevation Model (DEM) was used to create mask imagery to remove interfering factors such as buildings and water at low altitudes. Then, the mask imagery was diced into several small parts to reduce the large images' inconsistency, and they were used as the sources to be classified. After that, vector conversion was done on the classified imagery in ArcGIS. Finally, to ensure accuracy, other interfering factors such as buildings at high altitudes, bare land, and land covered by little vegetation were removed manually. Because the method can extract geological hazards in a short time, it is of great importance for decision-makers and rescuers who need to know the degree of damage in the disaster area, especially within 72 hours after an earthquake. Therefore, the method will play an important role in decision making, rescue, and disaster response planning

Full Text Available The digital time delay integration (digital TDI technology of the complementary metal-oxide-semiconductor (CMOS image sensor has been widely adopted and developed in the optical remotesensing field. However, the details of targets that have low illumination or low contrast in scenarios of high contrast are often drowned out because of the superposition of multi-stage images in digital domain multiplies the read noise and the dark noise, thus limiting the imaging dynamic range. Through an in-depth analysis of the information transfer model of digital TDI, this paper attempts to explore effective ways to overcome this issue. Based on the evaluation and analysis of multi-stage images, the entropy-maximized adaptive histogram equalization (EMAHE algorithm is proposed to improve the ability of images to express the details of dark or low-contrast targets. Furthermore, in this paper, an image fusion method is utilized based on gradient pyramid decomposition and entropy weighting of different TDI stage images, which can improve the detection ability of the digital TDI CMOS for complex scenes with high contrast, and obtain images that are suitable for recognition by the human eye. The experimental results show that the proposed methods can effectively improve the high-dynamic-range imaging (HDRI capability of the digital TDI CMOS. The obtained images have greater entropy and average gradients.

The Dead Sea coastal area is exposed to the destructive process of sinkhole collapse. The increase in sinkhole activity in the last two decades has been substantial, resulting from the continuous decrease in the Dead Sea's level, with more than 1,000 sinkholes developing as a result of upper layer collapse. Large sinkholes can reach 25 m in diameter. They are concentrated mainly in clusters in several dozens of sites with different characteristics. In this research, methods for mapping, monitoring and predicting sinkholes were developed using active and passive remote-sensingmethods: field spectrometer, geophysical ground penetration radar (GPR) and a frequency domain electromagnetic instrument (FDEM). The research was conducted in three stages: 1) literature review and data collection; 2) mapping regions abundant with sinkholes in various stages and regions vulnerable to sinkholes; 3) analyzing the data and translating it into cognitive and accessible scientific information. Field spectrometry enabled a comparison between the spectral signatures of soil samples collected near active or progressing sinkholes, and those collected in regions with no visual sign of sinkhole occurrence. FDEM and GPR investigations showed that electrical conductivity and soil moisture are higher in regions affected by sinkholes. Measurements taken at different time points over several seasons allowed monitoring the progress of an 'embryonic' sinkhole.

In previous work, we derived the adjoint method as a computationally efficient path to three-dimensional (3D) retrievals of clouds and aerosols. In this paper we will demonstrate the use of adjoint methods for retrieving two-dimensional (2D) fields of cloud extinction. The demonstration uses a new 2D radiative transfer solver (FSDOM). This radiation code was augmented with adjoint methods to allow efficient derivative calculations needed to retrieve cloud and surface properties from multi-angle reflectance measurements. The code was then used in three synthetic retrieval studies. Our retrieval algorithm adjusts the cloud extinction field and surface albedo to minimize the measurement misfit function with a gradient-based, quasi-Newton approach. At each step we compute the value of the misfit function and its gradient with two calls to the solver FSDOM. First we solve the forward radiative transfer equation to compute the residual misfit with measurements, and second we solve the adjoint radiative transfer equation to compute the gradient of the misfit function with respect to all unknowns. The synthetic retrieval studies verify that adjoint methods are scalable to retrieval problems with many measurements and unknowns. We can retrieve the vertically-integrated optical depth of moderately thick clouds as a function of the horizontal coordinate. It is also possible to retrieve the vertical profile of clouds that are separated by clear regions. The vertical profile retrievals improve for smaller cloud fractions. This leads to the conclusion that cloud edges actually increase the amount of information that is available for retrieving the vertical profile of clouds. However, to exploit this information one must retrieve the horizontally heterogeneous cloud properties with a 2D (or 3D) model. This prototype shows that adjoint methods can efficiently compute the gradient of the misfit function. This work paves the way for the application of similar methods to 3D remote

The application of Chahine's (1970) inversion technique to remotesensing problems utilizing the limb viewing geometry is discussed. The problem considered here involves occultation-type measurements and limb radiance-type measurements from either spacecraft or balloon platforms. The kernel matrix of the inversion problem is either an upper or lower triangular matrix. It is demonstrated that the Chahine inversion technique always converges, provided the diagonal elements of the kernel matrix are nonzero.

Full Text Available Surface soil moisture (SM plays a fundamental role in energy and water partitioning in the soil–plant–atmosphere continuum. A reliable and operational algorithm is much needed to retrieve regional surface SM at high spatial and temporal resolutions. Here, we provide an operational framework of estimating surface SM at fine spatial resolutions (using visible/thermal infrared images and concurrent meteorological data based on a trapezoidal space defined by remotelysensed vegetation cover (Fc and land surface temperature (LST. Theoretical solutions of the wet and dry edges were derived to achieve a more accurate and effective determination of the Fc/LST space. Subjectivity and uncertainty arising from visual examination of extreme boundaries can consequently be largely reduced. In addition, theoretical derivation of the extreme boundaries allows a per-pixel determination of the VI/LST space such that the assumption of uniform atmospheric forcing over the entire domain is no longer required. The developed approach was tested at the Tibetan Plateau Soil Moisture/Temperature Monitoring Network (SMTMN site in central Tibet, China, from August 2010 to August 2011 using Moderate Resolution Imaging Spectroradiometer (MODIS Terra images. Results indicate that the developed trapezoid model reproduced the spatial and temporal patterns of observed surface SM reasonably well, with showing a root-mean-square error of 0.06 m3·m−3 at the site level and 0.03 m3·m−3 at the regional scale. In addition, a case study on 2 September 2010 highlighted the importance of the theoretically calculated wet and dry edges, as they can effectively obviate subjectivity and uncertainties in determining the Fc/LST space arising from visual interpretation of satellite images. Compared with Land Surface Models (LSMs in Global Land Data Assimilation System-1, the remotesensing-based trapezoid approach gave generally better surface SM estimates, whereas the LSMs showed

Super resolution (SR) refers to generation of a High Resolution (HR) image from a decimated, blurred, low-resolution (LR) image set, which can be either a single frame or multi-frame that contains a collection of several images acquired from slightly different views of the same observation area. In this study, we propose a novel application of tri-stereo RemoteSensing (RS) satellite images to the super resolution problem. Since the tri-stereo RS images of the same observation area are acquired from three different viewing angles along the flight path of the satellite, these RS images are properly suited to a SR application. We first estimate registration between the chosen reference LR image and other LR images to calculate the sub pixel shifts among the LR images. Then, the warping, blurring and down sampling matrix operators are created as sparse matrices to avoid high memory and computational requirements, which would otherwise make the RS-SR solution impractical. Finally, the overall system matrix, which is constructed based on the obtained operator matrices is used to obtain the estimate HR image in one step in each iteration of the SR algorithm. Both the Laplacian and total variation regularizers are incorporated separately into our algorithm and the results are presented to demonstrate an improved quantitative performance against the standard interpolation method as well as improved qualitative results due expert evaluations.

Full Text Available Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remotesensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth, sensor and environmental noise, spectral resolution of the sensor and spectral variability of the benthic classes. In this paper, we present a simple hierarchical clustering method coupled with a shallow water forward model to generate water-column specific spectral libraries. This technique requires no prior decision on the number of classes to output: the resultant classes are optically separable above the spectral noise introduced by the sensor, image based radiometric corrections, the benthos’ natural spectral variability and the attenuating properties of a variable water column at depth. The modeling reveals the effect reducing the spectral resolution has on the number and type of classes that are optically distinct. We illustrate the potential of this clustering algorithm in an analysis of the conditions, including clustering accuracy, sensor spectral resolution and water column optical properties and depth that enabled the spectral distinction of the seagrass Amphibolis antartica from benthic algae.

To resolve the above-mentioned registration difficulties, a parallel and adaptive uniform-distributed registration method for CE-1 lunar remotesensed imagery is proposed in this paper. Based on 6 pairs of randomly selected images, both the standard SIFT algorithm and the parallel and adaptive uniform-distributed registration method were executed, the versatility and effectiveness were assessed. The experimental results indicate that: by applying the parallel and adaptive uniform-distributed registration method, the efficiency of CE-1 lunar remotesensed imagery registration were increased dramatically. Therefore, the proposed method in the paper could acquire uniform-distributed registration results more effectively, the registration difficulties including difficult to obtain results, time-consuming, non-uniform distribution could be successfully solved.

The RemoteSensing in Wind Energy report provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind it began in year 2008 at DTU Wind Energy (formerly Risø) during the first PhD Summer School: RemoteSensing in Wind Energy...... colleagues in the Meteorology and Test and Measurements Sections from DTU Wind Energy in the PhD Summer Schools. We hope to continue adding more topics in future editions and to update and improve as necessary, to provide a truly state-of-the-art ‘guideline’ available for people involved in RemoteSensing...

Scale problems are a major source of concern in the field of remotesensing. Since the remotesensing is a complex technology system, there is a lack of enough cognition on the connotation of scale and scale effect in remotesensing. Thus, this paper first introduces the connotations of pixel-based scale and summarizes the general understanding of pixel-based scale effect. Pixel-based scale effect analysis is essentially important for choosing the appropriate remotesensing data and the proper processing parameters. Fractal dimension is a useful measurement to analysis pixel-based scale. However in traditional fractal dimension calculation, the impact of spatial resolution is not considered, which leads that the scale effect change with spatial resolution can't be clearly reflected. Therefore, this paper proposes to use spatial resolution as the modified scale parameter of two fractal methods to further analyze the pixel-based scale effect. To verify the results of two modified methods (MFBM (Modified Windowed Fractal Brownian Motion Based on the Surface Area) and MDBM (Modified Windowed Double Blanket Method)); the existing scale effect analysis method (information entropy method) is used to evaluate. And six sub-regions of building areas and farmland areas were cut out from QuickBird images to be used as the experimental data. The results of the experiment show that both the fractal dimension and information entropy present the same trend with the decrease of spatial resolution, and some inflection points appear at the same feature scales. Further analysis shows that these feature scales (corresponding to the inflection points) are related to the actual sizes of the geo-object, which results in fewer mixed pixels in the image, and these inflection points are significantly indicative of the observed features. Therefore, the experiment results indicate that the modified fractal methods are effective to reflect the pixel-based scale effect existing in remotesensing

The article considers the issues of optimizing the use of remotesensing data. Built a mathematical model to describe the economic effect of the use of remotesensing data. It is shown that this model is incorrect optimisation task. Given a numerical method of solving this problem. Also discusses how to optimize organizational structure by using genetic algorithm based on remotesensing. The methods considered allow the use of remotesensing data in an optimal way. The proposed mathematical m...

Papers were presented in four subject areas: applications of remotesensing; data analysis, digital and analog; acquisition systems; and general. Abstracts of individual items from the conference were prepared separately for the data base

Full Text Available For this research, the researchers examine various existing image classification algorithms with the aim of demonstrating how these algorithms can be applied to remotesensing images. These algorithms are broadly divided into supervised...

Cornell's RemoteSensing Program has been involved in a continuing investigation to assess the value of remotesensing for vineyard management. Program staff members have conducted a series of site and crop analysis studies. These include: (1) panchromatic aerial photography for planning artificial drainage in a new vineyard; (2) color infrared aerial photography for assessing crop vigor/health; and (3) color infrared aerial photography and aircraft multispectral scanner data for evaluating yield related factors. These studies and their findings are reviewed.

To facilitate locating archaeological sites before they are compromised or destroyed, we are developing approaches for generating maps of probable archaeological sites, through detecting subtle anomalies in vegetative cover, soil chemistry, and soil moisture by analyzing remotelysensed data from multiple sources. We previously reported some success in this effort with a statistical analysis of slope, radar, and Ikonos data (including tasseled cap and NDVI transforms) with Student's t-test. We report here on new developments in our work, performing an analysis of 8-band multispectral Worldview-2 data. The Worldview-2 analysis begins by computing medians and median absolute deviations for the pixels in various annuli around each site of interest on the 28 band difference ratios. We then use principle components analysis followed by linear discriminant analysis to train a classifier which assigns a posterior probability that a location is an archaeological site. We tested the procedure using leave-one-out cross validation with a second leave-one-out step to choose parameters on a 9,859x23,000 subset of the WorldView-2 data over the western portion of Ft. Irwin, CA, USA. We used 100 known non-sites and trained one classifier for lithic sites (n=33) and one classifier for habitation sites (n=16). We then analyzed convex combinations of scores from the Archaeological Predictive Model (APM) and our scores. We found that that the combined scores had a higher area under the ROC curve than either individual method, indicating that including WorldView-2 data in analysis improved the predictive power of the provided APM.

Cloud detection is a necessary phase in satellite images processing to retrieve the atmospheric and lithospheric parameters. Currently, some cloud detection methods based on Random Forest (RF) model have been proposed but they do not consider both spectral and textural characteristics of the image. Furthermore, they have not been tested in the presence of snow/ice. In this paper, we introduce two RF based algorithms, Feature Level Fusion Random Forest (FLFRF) and Decision Level Fusion Random Forest (DLFRF) to incorporate visible, infrared (IR) and thermal spectral and textural features (FLFRF) including Gray Level Co-occurrence Matrix (GLCM) and Robust Extended Local Binary Pattern (RELBP_CI) or visible, IR and thermal classifiers (DLFRF) for highly accurate cloud detection on remotesensing images. FLFRF first fuses visible, IR and thermal features. Thereafter, it uses the RF model to classify pixels to cloud, snow/ice and background or thick cloud, thin cloud and background. DLFRF considers visible, IR and thermal features (both spectral and textural) separately and inserts each set of features to RF model. Then, it holds vote matrix of each run of the model. Finally, it fuses the classifiers using the majority vote method. To demonstrate the effectiveness of the proposed algorithms, 10 Terra MODIS and 15 Landsat 8 OLI/TIRS images with different spatial resolutions are used in this paper. Quantitative analyses are based on manually selected ground truth data. Results show that after adding RELBP_CI to input feature set cloud detection accuracy improves. Also, the average cloud kappa values of FLFRF and DLFRF on MODIS images (1 and 0.99) are higher than other machine learning methods, Linear Discriminate Analysis (LDA), Classification And Regression Tree (CART), K Nearest Neighbor (KNN) and Support Vector Machine (SVM) (0.96). The average snow/ice kappa values of FLFRF and DLFRF on MODIS images (1 and 0.85) are higher than other traditional methods. The

Soil moisture is one of the important hydrological elements. Obtaining soil moisture accurately and effectively is of great significance for water resource management in irrigation area. During the process of soil moisture content retrieval with multiremote sensing data, multi- remotesensing data always brings multi-spatial scale problems which results in inconformity of soil moisture content retrieved by remotesensing in different spatial scale. In addition, agricultural water use management has suitable spatial scale of soil moisture information so as to satisfy the demands of dynamic management of water use and water demand in certain unit. We have proposed to use land parcel unit as the minimum unit to do soil moisture content research in agricultural water using area, according to soil characteristics, vegetation coverage characteristics in underlying layer, and hydrological characteristic into the basis of study unit division. We have proposed division method of land parcel units. Based on multi thermal infrared and near infrared remotesensing data, we calculate the ndvi and tvdi index and make a statistical model between the tvdi index and soil moisture of ground monitoring station. Then we move forward to study soil moisture remotesensing retrieval method on land parcel unit scale. And the method has been applied in Hetao irrigation area. Results show that compared with pixel scale the soil moisture content in land parcel unit scale has displayed stronger correlation with true value. Hence, remotesensing retrieval method of soil moisture content in land parcel unit scale has shown good applicability in Hetao irrigation area. We converted the research unit into the scale of land parcel unit. Using the land parcel units with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data

Environmental pollution is a problem of international scope and concern. It can be subdivided into problems relating to water, air, or land pollution. Many of the problems in these three categories lend themselves to study and possible solution by remotesensing. Through the use of remotesensing systems and techniques, it is possible to detect and monitor, and in some cases, identify, measure, and study the effects of various environmental pollutants. As a guide for making decisions regarding the use of remote sensors for pollution studies, a special five-dimensional sensor/applications matrix has been designed. The matrix defines an environmental goal, ranks the various remotesensing objectives in terms of their ability to assist in solving environmental problems, lists the environmental problems, ranks the sensors that can be used for collecting data on each problem, and finally ranks the sensor platform options that are currently available.

Various aspects of remotesensing are discussed. Topics include: the EARTHNET data acquisition, processing, and distribution facility the design and implementation of a digital interactive image processing system geometrical aspects of remotesensing and space cartography remotesensing of a complex surface legal aspects of remotesensingremotesensing of pollution, dust storms, ice masses, and ocean waves and currents use of satellite images for weather forecasting. Notes on field trips and work-sheets for laboratory exercises are included.

Craters of the Moon (COTM) National Park, on the eastern Snake River Plain, and its associated lava fields are currently a focus of the NASA SSERVI FINESSE (Field Investigations to Enable Solar System Science and Exploration) team. COTM was selected for study owing to similarities with volcanic features observed on the Moon, Mars and Vesta. The COTM basaltic lava fields emanate from an 80 km long rift zone where at least eight eruptive episodes, occurring 15,000 to 2,000 BP, have created an expansive volcanic field covering an area of approximately 1,650 km2. This polygenetic volcanic field hosts a diverse collection of basaltic volcanic edifices such as phreatic explosion craters, eruptive fissures, cinder cones, spatter cones, shield volcanoes and expansive lava flows. Engineering challenges and high cost limit the number of robotic and human field investigations of planetary bodies and, due to these constraints, exhaustive remotesensing investigations of planetary surface properties are undertaken prior to field deployment. This creates an unavoidable dependence upon remotesensing, a critical difference between field investigations of planetary bodies and most terrestrial field investigations. Studies of this nature have utility in terrestrial investigations as they can help link spatially encompassing datasets and conserve field resources. We present preliminary results utilizing Earth orbital datasets to determine the efficacy of products derived from remotelysensed data when compared to geologic field observations. Multispectral imaging data (ASTER, AVIRIS, TIMS) collected at a range of spatial and spectral resolutions are paired with high resolution imagery from both orbit and unmanned aircraft systems. This enables the creation of derived products detailing morphology, compositional variation, mineralogy, relative age and vegetation. The surface morphology of flows within COTM differs from flow to flow and observations of these properties can aid in

Everybody uses a bulb to illustrate an idea but nobody shows where the current comes from. Majority of remotesensing user community comes from natural and social sciences domain while remotesensing technology evolves from physical and engineering sciences. To ensure inculcation and internalization of remotesensing technology by application/resource scientists, trainer needs to transfer physical and engineering concepts in geometric manner. Here, the steering for the transfer of knowledge (facts, procedures, concepts and principles) and skills (thinking, acting, reacting and interacting) needs to take the trainees from Known to Unknown, Concrete to Abstract, Observation to Theory and Simple to Complex. In the initial stage of training/education, experiential learning by instructor led exploring of thematic details in false colour composite (FCC) as well as in individual black and white spectral band(s) imagery by trainees not only creates interest, confidence build-up and orientation towards purposeful learning but also helps them to overcome their inhibitions towards the physical and engineering basal. The methodology to be adopted has to inculcate productive learning, emphasizing more on thinking and trial and error aspects as opposed to reproductive learning based dominantly on being told and imitation. The delivery by trainer needs to ensure dynamic, stimulating and effective discussions through deluging questions pertaining to analysis, synthesis and evaluation nature. This would ensure proactive participation from trainees. Hands-on module leads to creative concretization of concepts. To keep the trainees inspired to learn in an auto mode during post-training period, they need to consciously swim in the current and emerging knowledge pool during training programme. This is achieved through assignment of seminar delivery task to the trainees. During the delivery of seminar, peers and co-trainees drive the trainee to communicate the seminar content not only

Current remotesensing studies of phenology have been limited to coarse spatial or temporal resolution and often lack a direct link to field measurements. To address this gap, we compared remotesensing methodologies using Landsat Thematic Mapper (TM) imagery to extensive field measurements in a mixed northern hardwood forest. Five vegetation indices, five mathematical...

Earth and its environment are studied by different scientific disciplines as geosciences, science of engineering, social sciences, geography, etc. The study of the above, beyond pure scientific interest, is useful for the practical needs of man. Photogrammetry and RemoteSensing (defined by Statute II of ISPRS) is the art, science, and technology of obtaining reliable information from non-contact imaging and other sensor systems about the Earth and its environment, and other physical objects and of processes through recording, measuring, analyzing and representation. Therefore, according to this definition, photogrammetry and remotesensing can support studies of the above disciplines for acquisition of geoinformation. This paper concerns basic concepts of geosciences (geomorphology, geology, hydrology etc), and the fundamentals of photogrammetry-remotesensing, in order to aid the understanding of the relationship between photogrammetry-remotesensing and geoinformation and also structure curriculum in a brief, concise and coherent way. This curriculum can represent an appropriate research and educational outline and help to disseminate knowledge in various directions and levels. It resulted from our research and educational experience in graduate and post-graduate level (post-graduate studies relative to the protection of environment and protection of monuments and historical centers) in the Lab. of Photogrammetry - RemoteSensing in Civil Engineering Faculty of Aristotle University of Thessaloniki.

Evaporation from the water surface of a reservoir can significantly affect its function of ensuring the availability and temporal stability of water supply. Current estimations of reservoir evaporative loss are dependent on water area derived from a reservoir storage-area curve. Such curves are unavailable if the reservoir is located in a data-sparse region or questionable if long-term sedimentation has changed the original elevation-area relationship. We propose a remotesensing framework to estimate reservoir evaporative loss at the regional scale. This framework uses a multispectral water index to extract reservoir area from Landsat imagery and estimate monthly evaporation volume based on pan-derived evaporative rates. The optimal index threshold is determined based on local observations and extended to unobserved locations and periods. Built on the cloud computing capacity of the Google Earth Engine, this framework can efficiently analyze satellite images at large spatiotemporal scales, where such analysis is infeasible with a single computer. Our study involves 200 major reservoirs in Texas, captured in 17,811 Landsat images over a 32-year period. The results show that these reservoirs contribute to an annual evaporative loss of 8.0 billion cubic meters, equivalent to 20% of their total active storage or 53% of total annual water use in Texas. At five coastal basins, reservoir evaporative losses exceed the minimum freshwater inflows required to sustain ecosystem health and fishery productivity of the receiving estuaries. Reservoir evaporative loss can be significant enough to counterbalance the positive effects of impounding water and to offset the contribution of water conservation and reuse practices. Our results also reveal the spatially variable performance of the multispectral water index and indicate the limitation of using scene-level cloud cover to screen satellite images. This study demonstrates the advantage of combining satellite remotesensing and

The RemoteSensing in Wind Energy Compendium provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind this compendium began in year 2008 at Risø DTU during the first PhD Summer School: RemoteSensing in Wind Energy. Thus...... in the Meteorology and Test and Measurements Programs from the Wind Energy Division at Risø DTU in the PhD Summer Schools. We hope to add more topics in future editions and to update as necessary, to provide a truly state-of-the-art compendium available for people involved in RemoteSensing in Wind Energy....

Full Text Available Inspired by human visual cognitive mechanism,a method of residential area extraction from high-resolution remotesensing images was proposed based on visual saliency and perceptual organization. Firstly,the data field theory of cognitive physics was introduced to model the visual saliency and the candidate residential areas were produced by adaptive thresholding. Then,the exact residential areas were obtained and refined by perceptual organization based on the high-frequency features of multi-scale wavelet transform. Finally,the validity of the proposed method was verified by experiments conducted on ZY-3 and Quickbird image data sets.

Full Text Available Technological advancements in remotesensing and GIS have improved natural resource managers' abilities to monitor large-scale disturbances. In a time where many processes are heading towards automation, this study has regressed to simple techniques to bridge a gap found in the advancement of technology. The near-daily monitoring of dredge plume extent is common practice using Moderate Resolution Imaging Spectroradiometer (MODIS imagery and associated algorithms to predict the total suspended solids (TSS concentration in the surface waters originating from floods and dredge plumes. Unfortunately, these methods cannot determine the difference between dredge plume and benthic features in shallow, clear water. This case study at Barrow Island, Western Australia, uses hand digitising to demonstrate the ability of human interpretation to determine this difference with a level of confidence and compares the method to contemporary TSS methods. Hand digitising was quick, cheap and required very little training of staff to complete. Results of ANOSIM R statistics show remotesensing derived TSS provided similar spatial results if they were thresholded to at least 3 mg L(-1. However, remotesensing derived TSS consistently provided false-positive readings of shallow benthic features as Plume with a threshold up to TSS of 6 mg L(-1, and began providing false-negatives (excluding actual plume at a threshold as low as 4 mg L(-1. Semi-automated processes that estimate plume concentration and distinguish between plumes and shallow benthic features without the arbitrary nature of human interpretation would be preferred as a plume monitoring method. However, at this stage, the hand digitising method is very useful and is more accurate at determining plume boundaries over shallow benthic features and is accessible to all levels of management with basic training.

Remotesensing is defined as data acquisition of an object, deprived physical contact. Fundamentally, most remotesensing applications are referred to as the use of satellite- or aircraft-based sensor technologies to detect and classify objects mainly on Earth or other planets. In the last years there have been efforts to bring the important subject of remotesensing into schools, however, most of these attempts focused on geography disciplines - restricting to the applications of remotesensing and to a less extent the technique itself and the physics behind it. Optical remotesensing is based on physical principles and technical devices, which are very meaningful from a theoretical point of view as well as for "hands-on" teaching. Some main subjects are radiation, atom and molecular physics, spectroscopy, as well as optics and the semiconductor technology used in modern digital cameras. Thus two objectives were outlined for this project: 1) to investigate the possibilities of using remotesensing techniques in physics teaching, and 2) to identify its impact on pupil's interest in the field of natural sciences. This joint project of the DLR_School_Lab, Oberpfaffenhofen of the German Aerospace Center (DLR) and the Earth and Planetary Image Facility (EPIF) at BGU, was conducted in 2016. Thirty teenagers (ages 16-18) participated in the project and were exposed to the cutting edge methods of earth observation. The pupils on both sides participated in the project voluntarily, knowing that at least some of the project's work had to be done in their leisure time. The pupil's project started with a day at EPIF and DLR respectively, where the project task was explained to the participants and an introduction to remotesensing of vegetation was given. This was realized in lectures and in experimental workshops. During the following two months both groups took several measurements with modern optical remotesensing systems in their home region with a special focus on flora

To support the operational use of Synthetic Aperture Radar (SAR) earth observation systems, the European Space Agency (ESA) is developing Sentinel-1 radar satellites operating in C-band. Much like its SAR predecessors (Earth Resource Satellite, ENVISAT, and RADARSAT), the Sentinel-1 will operate at a medium spatial resolution (ranging from 5 to 40 m), but with a greatly improved revisit period, especially over Europe (∼2 days). Given the planned high temporal sampling and the operational configuration Sentinel-1 is expected to be beneficial for operational monitoring of dynamic processes in hydrology and phenology. The benefit of a C-band SAR monitoring service in hydrology has already been demonstrated within the scope of the Soil Moisture for Hydrometeorologic Applications (SHARE) project using data from the Global Mode (GM) of the Advanced Synthetic Aperture Radar (ASAR). To fully exploit the potential of the SAR soil moisture products, well characterized error needs to be provided with the products. Understanding errors of remotelysensed surface soil moisture (SSM) datasets was indispensible for their application in models, for extractions of blended SSM products, as well as for their usage in evaluation of other soil moisture datasets. This thesis has several objectives. First, it provides the basics and state of the art methods for evaluating measures of SSM, including both the standard (e.g. Root Mean Square Error, Correlation coefficient) and the advanced (e.g. Error propagation, Triple collocation) evaluation measures. A summary of applications of soil moisture datasets is presented and evaluation measures are suggested for each application according to its requirement on the dataset quality. The evaluation of the Advanced Synthetic Aperture Radar (ASAR) Global Mode (GM) SSM using the standard and advanced evaluation measures comprises a second objective of the work. To achieve the second objective, the data from the Australian Water Assessment System

River discharge is a prerequisite for an understanding of flood hazard and water resource management, yet we have poor knowledge of it, especially over remote basins. Previous studies have successfully used a classic hydraulic geometry, at-many-stations hydraulic geometry (AMHG), and Manning's equation to estimate the river discharge. Theoretical bases of these empirical methods were introduced by Leopold and Maddock (1953) and Manning (1889), and those have been long used in the field of hydrology, water resources, and geomorphology. However, the methods to estimate the river discharge from remotelysensed data essentially require bathymetric information of the river or are not applicable to braided rivers. Furthermore, the methods used in the previous studies adopted assumptions of river conditions to be steady and uniform. Consequently, those methods have limitations in estimating the river discharge in complex and unsteady flow in nature. In this study, we developed a novel approach to estimating river discharges by applying the weak learner method (here termed WLQ), which is one of the ensemble methods using multiple classifiers, to the remotelysensed measurements of water levels from Envisat altimetry, effective river widths from PALSAR images, and multi-temporal surface water slopes over a part of the mainstem Congo. Compared with the methods used in the previous studies, the root mean square error (RMSE) decreased from 5,089 m3s-1 to 3,701 m3s-1, and the relative RMSE (RRMSE) improved from 12% to 8%. It is expected that our method can provide improved estimates of river discharges in complex and unsteady flow conditions based on the data-driven prediction model by machine learning (i.e. WLQ), even when the bathymetric data is not available or in case of the braided rivers. Moreover, it is also expected that the WLQ can be applied to the measurements of river levels, slopes and widths from the future Surface Water Ocean Topography (SWOT) mission to be

With the rapid development of optical remotesensing satellites, ship detection and identification based on large-scale remotesensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance and maritime management. However, because the harbor environment is complex, gray information and texture features between docked ships and their connected dock regions are indistinguishable, most of the popular detection methods are limited by their calculation efficiency and detection accuracy. In this paper, a novel hierarchical method that combines an efficient candidate scanning strategy and an accurate candidate identification mixture model is presented for inshore ship detection in complex harbor areas. First, in the candidate region extraction phase, an omnidirectional intersected two-dimension scanning (OITDS) strategy is designed to rapidly extract candidate regions from the land-water segmented images. In the candidate region identification phase, a decision mixture model (DMM) is proposed to identify real ships from candidate objects. Specifically, to improve the robustness regarding the diversity of ships, a deformable part model (DPM) was employed to train a key part sub-model and a whole ship sub-model. Furthermore, to improve the identification accuracy, a surrounding correlation context sub-model is built. Finally, to increase the accuracy of candidate region identification, these three sub-models are integrated into the proposed DMM. Experiments were performed on numerous large-scale harbor remotesensing images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency.

The rapid technologic advances in the scientific areas of photogrammetry and remotesensing require continuous readjustments at the educational programs and their implementation. The teaching teamwork should deal with the challenge to offer the volume of the knowledge without preventing the understanding of principles and methods and also to introduce "new" knowledge (advances, trends) followed by evaluation and presentation of relevant applications. This is of particular importance for a Civil Engineering Faculty as this in Aristotle University of Thessaloniki, as the framework of Photogrammetry and RemoteSensing is closely connected with applications in the four educational Divisions of the Faculty. This paper refers to the above and includes subjects of organizing the courses in photogrammetry and remotesensing in the Civil Engineering Faculty of Aristotle University of Thessaloniki. A scheme of the general curriculum as well the teaching aims and methods are also presented.

Remotesensing, as a tool to aid in the control of water pollution, offers a means of making rapid, economical surveys of areas that are relatively inaccessible on the ground. At the same time, it offers the only practical means of mapping pollution patterns that cover large areas. Detection of oil slicks, thermal pollution, sewage, and algae are discussed.

Aerosols are solid or liquid particles suspended in the air, and those observed by satellite remotesensing are typically between about 0.05 and 10 microns in size. (Note that in traditional aerosol science, the term "aerosol" refers to both the particles and the medium in which they reside, whereas for remotesensing, the term commonly refers to the particles only. In this article, we adopt the remote-sensing definition.) They originate from a great diversity of sources, such as wildfires, volcanoes, soils and desert sands, breaking waves, natural biological activity, agricultural burning, cement production, and fossil fuel combustion. They typically remain in the atmosphere from several days to a week or more, and some travel great distances before returning to Earth's surface via gravitational settling or washout by precipitation. Many aerosol sources exhibit strong seasonal variability, and most experience inter-annual fluctuations. As such, the frequent, global coverage that space-based aerosol remote-sensing instruments can provide is making increasingly important contributions to regional and larger-scale aerosol studies.

Cyanobacterial harmful algal blooms (CyanoHAB) are thought to be increasing globally over the past few decades, but relatively little quantitative information is available about the spatial extent of blooms. Satellite remotesensing provides a potential technology for identifying...

Full Text Available Triggered by earthquakes, rainfall, or anthropogenic activities, landslides represent widespread and problematic geohazards worldwide. In recent years, multiple remotesensing techniques, including synthetic aperture radar, optical, and light detection and ranging measurements from spaceborne, airborne, and ground-based platforms, have been widely applied for the analysis of landslide processes. Current techniques include landslide detection, inventory mapping, surface deformation monitoring, trigger factor analysis and mechanism inversion. In addition, landslide susceptibility modelling, hazard assessment, and risk evaluation can be further analyzed using a synergic fusion of multiple remotesensing data and other factors affecting landslides. We summarize the 19 articles collected in this special issue of RemoteSensing of Landslide, in the terms of data, methods and applications used in the papers.

RemoteSensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remotesensing techniques, design and applications published in RemoteSensing. We are pleased to announce the first “RemoteSensing Best Paper Award” for 2013. Nominations were selected by the Editor-in-Chief and selected editorial board members from among all the papers published in 2009. Reviews and research papers were evaluated separately.

Remotesensing of land-surface phenology is an important method for studying the patterns of plant and animal growth cycles. Phenological events are sensitive to climate variation; therefore phenology data provide important baseline information documenting trends in ecology and detecting the impacts of climate change on multiple scales. The USGS Remotesensing of land surface phenology program produces annually, nine phenology indicator variables at 250 m and 1,000 m resolution for the contiguous U.S. The 12 year archive is available at http://phenology.cr.usgs.gov/index.php.

Mapping environmental envelopes onto geographical space has been classically important for understanding biogeographical patterns. Knowing the biotic and abiotic limits defining these envelopes, we can better understand the requirements limiting species distributions. Most present efforts in this regard have focused on single-species distribution models, but the current breadth and accessibility of quantitative, spatially explicit environmental information can also be explored from an environment-first perspective. We thus used remotesensing to determine the occurrence of environmental discontinuities in the Amazon region and evaluated if such discontinuities may act as barriers to determine species distribution and range limits, forming clear environmental envelopes. We combined data on topography (SRTM), precipitation (CHIRPS), vegetation descriptors (PALSAR-1 backscattering, biomass, NDVI) and temperature (MODIS), using object-based image analysis and unsupervised learning to map environmental envelopes. We identified 14 environmental envelopes for the Amazon sensu latissimo region, mainly delimited by changes in vegetation, topography and precipitation. The resulting envelopes were compared to the distribution of 120 species of Trogonidae, Galbulidae, Bucconidae, Cebidae, Hylidae and Lecythidaceae, amounting to 22,649 occurrence records within the Amazonregion. We determined species prevalence in each envelope by calculating the ratio between species relative frequency per envelope and envelope relative frequency (area) in the complete map. Values closer to 1 indicate a high degree of prevalence. We found strong envelope associations (prevalence > 0.5) for 20 species (17% of analyzed taxa). Although several biogeographical and ecological factors will influence the distribution of a species, our results show that not only geographical barriers, but also modern environmental discontinuities may limit the distribution of some species., and may have also done so

This volume contains the proceedings of SPIE's remotesensing symposium which was held September 22--24, 1998, in Barcelona, Spain. Topics of discussion include the following: calibration techniques for soil moisture measurements; remotesensing of grasslands and biomass estimation of meadows; evaluation of agricultural disasters; monitoring of industrial and natural radioactive elements; and remotesensing of vegetation and of forest fires

Atmospheric aerosol particles affect the atmosphere's radiation balance by scattering and absorbing sunlight. Moreover, the particles act as condensation nuclei for clouds and affect their reflectivity. In addition, aerosols have negative health effects and they reduce visibility. Aerosols are emitted into the atmosphere from both natural and anthropogenic sources. Different types of aerosols have different effects on the radiation balance, thus global monitoring and typing of aerosols is of vital importance. In this thesis, several remotesensingmethods used in the measurement of atmospheric aerosols are evaluated. Remotesensing of aerosols can be done with active and passive instruments. Passive instruments measure radiation emitted by the sun and the Earth while active instruments have their own radiation source, for example a black body radiator or laser. The instruments utilized in these studies were sun photometers (PFR, Cimel), lidars (POLLYXT, CALIOP), transmissiometer (OLAF) and a spectroradiometer (MODIS). Retrieval results from spaceborne instruments (MODIS, CALIOP) were evaluated with ground based measurements (PFR, Cimel). In addition, effects of indicative aerosol model assumptions on the calculated radiative transfer were studied. Finally, aerosol particle mass at the ground level was approximated from satellite measurements and vertical profiles of aerosols measured with a lidar were analyzed. For the evaluation part, these studies show that the calculation of aerosol induced attenuation of radiation based on aerosol size distribution measurements is not a trivial task. In addition to dry aerosol size distribution, the effect of ambient relative humidity on the size distribution and the optical properties of the aerosols need to be known in order to achieve correct results from the calculations. Furthermore, the results suggest that aerosol size parameters retrieved from passive spaceborne measurements depend heavily on surgace reflectance

For complex systems, sufficient a priori knowledge is often lacking about the mathematical or empirical relationship between cause and effect or between inputs and outputs of a given system. Automated machine learning may offer a useful solution in such cases. Coastal marine optical environments represent such a case, as the optical remotesensing inverse problem remains largely unsolved. A self-organizing, cybernetic mathematical modeling approach known as the group method of data handling (GMDH), a type of statistical learning network (SLN), was used to generate explicit spectral inversion models for optically shallow coastal waters. Optically shallow water light fields represent a particularly difficult challenge in oceanographic remotesensing. Several algorithm-input data treatment combinations were utilized in multiple experiments to automatically generate inverse solutions for various inherent optical property (IOP), bottom optical property (BOP), constituent concentration, and bottom depth estimations. The objective was to identify the optimal remote-sensing reflectance Rrs(lambda) inversion algorithm. The GMDH also has the potential of inductive discovery of physical hydro-optical laws. Simulated data were used to develop generalized, quasi-universal relationships. The Hydrolight numerical forward model, based on radiative transfer theory, was used to compute simulated above-water remote-sensing reflectance Rrs(lambda) psuedodata, matching the spectral channels and resolution of the experimental Naval Research Laboratory Ocean PHILLS (Portable Hyperspectral Imager for Low-Light Spectroscopy) sensor. The input-output pairs were for GMDH and artificial neural network (ANN) model development, the latter of which was used as a baseline, or control, algorithm. Both types of models were applied to in situ and aircraft data. Also, in situ spectroradiometer-derived Rrs(lambda) were used as input to an optimization-based inversion procedure. Target variables

Full Text Available The technological developments in remotesensing (RS during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remotesensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications. Based on prior paper of the authors (in cooperation within the RemoteSensing Data Quality working group of ISPRS, which established a taxonomy on the dimensions of data quality in GIS and remotesensing domains, this paper is aiming at focusing on measures of uncertainty in remotesensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users’ needs. The present paper gives the theoretic overview of the issue, besides

The technological developments in remotesensing (RS) during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remotesensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications. Based on prior paper of the authors (in cooperation within the RemoteSensing Data Quality working group of ISPRS), which established a taxonomy on the dimensions of data quality in GIS and remotesensing domains, this paper is aiming at focusing on measures of uncertainty in remotesensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users' needs. The present paper gives the theoretic overview of the issue, besides selected, practice

Remotesensing is a kind of very effective method which can be used in all stages of geological prospecting. Geological prospecting with remotesensingmethod must be based on different genetic models of ore deposits, characteristics of geology-landscape and comprehensive analysis for geophysical and geochemical data, that is, by way of conceptual model prospecting. The prospecting results based on remotesensing geology should be assessed from three aspects such as direct, indirect and potential ones

A preliminary reconnaissance is being carried out to study the methods and procedures most useful for the detection of vegetation stress resulting from the various forms of environmental pollution, in the industrial area of Teesside, NE England, by means of a multiband remotesensing programme.

This book contains a detailed presentation of general principles of sensitivity analysis as well as their applications to sample cases of remotesensing experiments. An emphasis is made on applications of adjoint problems, because they are more efficient in many practical cases, although their formulation may seem counterintuitive to a beginner. Special attention is paid to forward problems based on higher-order partial differential equations, where a novel matrix operator approach to formulation of corresponding adjoint problems is presented. Sensitivity analysis (SA) serves for quantitative models of physical objects the same purpose, as differential calculus does for functions. SA provides derivatives of model output parameters (observables) with respect to input parameters. In remotesensing SA provides computer-efficient means to compute the jacobians, matrices of partial derivatives of observables with respect to the geophysical parameters of interest. The jacobians are used to solve corresponding inver...

This book is a collection of overview articles showing how space-based observations, combined with hydrological modeling, have considerably improved our knowledge of the continental water cycle and its sensitivity to climate change. Two main issues are highlighted: (1) the use in combination of space observations for monitoring water storage changes in river basins worldwide, and (2) the use of space data in hydrological modeling either through data assimilation or as external constraints. The water resources aspect is also addressed, as well as the impacts of direct anthropogenic forcing on land hydrology (e.g. ground water depletion, dam building on rivers, crop irrigation, changes in land use and agricultural practices, etc.). Remotesensing observations offer important new information on this important topic as well, which is highly useful for achieving water management objectives. Over the past 15 years, remotesensing techniques have increasingly demonstrated their capability to monitor components of th...

Rice is a dominant food crop of Bangladesh accounting about 75 percent of agricultural land use for rice cultivation and currently Bangladesh is the world's fourth largest rice producing country. Rice provides about two-third of total calorie supply and about one-half of the agricultural GDP and one-sixth of the national income in Bangladesh. Aus is one of the main rice varieties in Bangladesh. Crop production, especially rice, the main food staple, is the most susceptible to climate change and variability. Any change in climate will, thus, increase uncertainty regarding rice production as climate is major cause year-to-year variability in rice productivity. This paper shows the application of remotesensing data for estimating Aus rice yield in Bangladesh using official statistics of rice yield with real time acquired satellite data from Advanced Very High Resolution Radiometer (AVHRR) sensor and Principal Component Regression (PCR) method was used to construct a model. The simulated result was compared with official agricultural statistics showing that the error of estimation of Aus rice yield was less than 10%. Remotesensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.

Full Text Available Timely and accurate change detection of buildings provides important information for urban planning and management.Accompanying with the rapid development of satellite remotesensing technology,detecting building changes from high-resolution remotesensing images have received wide attention.Given that pixel-based methods of change detection often lead to low accuracy while object-based methods are complicated for uses,this research proposes a method that combines pixel-based and object-based methods for detecting building changes from high-resolution remotesensing images.First,based on the multiple features extracted from the high-resolution images,a random forest classifier is applied to detect changed building at the pixel level.Then,a segmentation method is applied to segement the post-phase remotesensing image and to get post-phase image objects.Finally,both changed building at the pixel level and post-phase image objects are fused to recognize the changed building objects.Multi-temporal QuickBird images are used as experiment data for building change detection with high-resolution remotesensing images,the results indicate that the proposed method could reduce the influence of environmental difference,such as light intensity and view angle,on building change detection,and effectively improve the accuracies of building change detection.

A knowledge in near real time, of the surface drag coefficient for drifting pack ice is vital for predicting its motions. And since this is not routinely available from measurements it must be replaced by estimates. Hence, a method for estimating this variable, as well as the drag coefficient at the water/ice interface and the ice thickness, for drifting open pack ice was developed. These estimates were derived from three-day sequences of LANDSAT-1 MSS images and surface weather charts and from the observed minima and maxima of these variables. The method was tested with four data sets in the southeastern Beaufort sea. Acceptable results were obtained for three data sets. Routine application of the method depends on the availability of data from an all-weather air or spaceborne remotesensing system, producing images with high geometric fidelity and high resolution.

This document is the final report summarizing research conducted by the RemoteSensing Research Unit, Department of Geography, University of California, Santa Barbara under National Aeronautics and Space Administration Research Grant NAG5-10457. This document describes work performed during the period of 1 March 2001 thorough 30 September 2002. This report includes a survey of research proposed and performed within RSRU and the UCSB Geography Department during the past 25 years. A broad suite of RSRU research conducted under NAG5-10457 is also described under themes of Applied Research Activities and Information Science Research. This research includes: 1. NASA ESA Research Grant Performance Metrics Reporting. 2. Global Data Set Thematic Accuracy Analysis. 3. ISCGM/Global Map Project Support. 4. Cooperative International Activities. 5. User Model Study of Global Environmental Data Sets. 6. Global Spatial Data Infrastructure. 7. CIESIN Collaboration. 8. On the Value of Coordinating Landsat Operations. 10. The California Marine Protected Areas Database: Compilation and Accuracy Issues. 11. Assessing Landslide Hazard Over a 130-Year Period for La Conchita, California RemoteSensing and Spatial Metrics for Applied Urban Area Analysis, including: (1) IKONOS Data Processing for Urban Analysis. (2) Image Segmentation and Object Oriented Classification. (3) Spectral Properties of Urban Materials. (4) Spatial Scale in Urban Mapping. (5) Variable Scale Spatial and Temporal Urban Growth Signatures. (6) Interpretation and Verification of SLEUTH Modeling Results. (7) Spatial Land Cover Pattern Analysis for Representing Urban Land Use and Socioeconomic Structures. 12. Colorado River Flood Plain RemoteSensing Study Support. 13. African Rainfall Modeling and Assessment. 14. RemoteSensing and GIS Integration.

The RemoteSensing in Wind Energy report provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind it began in year 2008 at DTU Wind Energy (formerly Risoe) during the first PhD Summer School: RemoteSensing in Wind Energy. Thus it is closely linked to the PhD Summer Schools where state-of-the-art is presented during the lecture sessions. The advantage of the report is to supplement with in-depth, article style information. Thus we strive to provide link from the lectures, field demonstrations, and hands-on exercises to theory. The report will allow alumni to trace back details after the course and benefit from the collection of information. This is the third edition of the report (first externally available), after very successful and demanded first two, and we warmly acknowledge all the contributing authors for their work in the writing of the chapters, and we also acknowledge all our colleagues in the Meteorology and Test and Measurements Sections from DTU Wind Energy in the PhD Summer Schools. We hope to continue adding more topics in future editions and to update and improve as necessary, to provide a truly state-of-the-art 'guideline' available for people involved in RemoteSensing in Wind Energy. (Author)

We present two recent instrument technology developments at NASA, Fluid Lensing and MiDAR, and their application to remotesensing of Earth's aquatic systems. Fluid Lensing is the first remotesensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions. MiDAR is a next-generation active hyperspectral remotesensing and optical communications instrument capable of active fluid lensing. Fluid Lensing has been used to provide 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) to enable a new method for remotesensing of living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an objectâ€"TM"s non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotelysensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remotesensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Previously, MiDAR was developed into a TRL 2 laboratory instrument capable of imaging in thirty-two narrowband channels across the VNIR spectrum (400-950nm). Recently, MiDAR UV was raised to TRL4 and expanded to include five ultraviolet bands from 280-400nm, permitting UV remotesensing capabilities in UV A, B, and C bands and enabling mineral identification and stimulated fluorescence measurements of organic proteins and compounds, such as green fluorescent proteins in terrestrial and

Full Text Available U ovom radu predstavljeni su različiti načini identifikovanja promjena kod daljinskih istraživanja. Različiti autori su predstavljali različite metode otkrivanja promjena na površini zemlje. Otkrivanje promjena je, između ostalog, veoma važno zbog praćenja promjena, kao i procjene promjena i međusobnih odnosa prirodnih i vještačkih objekata. Sve to vodi ka boljem razumijevanju potencijalnih uzroka promjena. : In this paper, the different ways to identify changes in remotesensing are given. Various authors have presented different methods of detecting changes on the Earth's surface. Detection of changes, among other things, are very important for tracking changes, as well as assessment and evaluation of changes and interrelations of natural and artificial objects. All this leads to better understanding of potential causes of change.

Full Text Available Mangrove ecosystems dominate the coastal wetlands of tropical and subtropical regions throughout the world. They provide various ecological and economical ecosystem services contributing to coastal erosion protection, water filtration, provision of areas for fish and shrimp breeding, provision of building material and medicinal ingredients, and the attraction of tourists, amongst many other factors. At the same time, mangroves belong to the most threatened and vulnerable ecosystems worldwide and experienced a dramatic decline during the last half century. International programs, such as the Ramsar Convention on Wetlands or the Kyoto Protocol, underscore the importance of immediate protection measures and conservation activities to prevent the further loss of mangroves. In this context, remotesensing is the tool of choice to provide spatio-temporal information on mangrove ecosystem distribution, species differentiation, health status, and ongoing changes of mangrove populations. Such studies can be based on various sensors, ranging from aerial photography to high- and medium-resolution optical imagery and from hyperspectral data to active microwave (SAR data. Remote-sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and changes during the last two decades, which is reflected by the large number of scientific papers published on this topic. To our knowledge, a recent review paper on the remotesensing of mangroves does not exist, although mangrove ecosystems have become the focus of attention in the context of current climate change and discussions of the services provided by these ecosystems. Also, climate change-related remote-sensing studies in coastal zones have increased drastically in recent years. The aim of this review paper is to provide a comprehensive overview and sound summary of all of the work undertaken, addressing the variety of remotelysensed data applied for mangrove

Remotesensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remotesensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remotesensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remotesensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remotesensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remotesensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

Remotesensing techniques enable quantitative information about a field trial to be obtained instantaneously and non-destructively. The aim of this study was to identify a method that can reduce inaccuracies in field trial analysis, and to identify how remotesensing can support and/or

Internationally, a number of studies have successfully used remotesensing technology to monitor forest damage. Remotesensing technology allows for instantaneous methods of assessments whereby ground assessments would be impossible on a regular basis. This paper provides an overview of how advances in ...

Progress is reported on three tasks designed to develop remotesensing beach reconnaissance techniques applicable to the benthic, beach intertidal...and beach upland zones. Task 1 is designed to develop remotesensing indicators of important beach composition and physical parameters which will...ultimately prove useful in models to predict beach conditions. Task 2 is designed to develop remotesensing techniques for survey of bottom features in

The issue of data quality (DQ) is of growing importance in RemoteSensing (RS), due to the widespread use of digital services (incl. apps) that exploit remotesensing data. In this position paper a body of experts from the ISPRS Intercommission working group III/IVb "DQ" identifies, categorises and reasons about issues that are considered as crucial for a RS research and application agenda. This ISPRS initiative ensures to build on earlier work by other organisations such as IEEE, CEOS or GEO, in particular on the meritorious work of the Quality Assurance Framework for Earth Observation (QA4EO) which was established and endorsed by the Committee on Earth Observation Satellites (CEOS) but aims to broaden the view by including experts from computer science and particularly database science. The main activities and outcomes include: providing a taxonomy of DQ dimensions in the RS domain, achieving a global approach to DQ for heterogeneous-format RS data sets, investigate DQ dimensions in use, conceive a methodology for managing cost effective solutions on DQ in RS initiatives, and to address future challenges on RS DQ dimensions arising in the new era of the big Earth data.

FORMOSAT-2 is Taiwan's first remotesensing satellite (RSS). It was launched on 20 May 2004 with five-year mission life and a very unique mission orbit at 891 km altitude. This orbit gives FORMOSAT-2 the daily revisit feature and the capability of imaging the Arctic and Antarctic regions due to the high enough altitude. For more than three years, FORMOSAT-2 has performed outstanding jobs and its global effectiveness is evidenced in many fields such as public education in Taiwan, Earth science and ecological niche research, preservation of the world heritages, contribution to the International Charter: space and major disasters, observation of suspected North Korea and Iranian nuclear facilities, and scientific observation of the atmospheric transient luminous events (TLEs). In order to continue the provision of earth observation images from space, the National Space Organization (NSPO) of Taiwan started to work on the second RSS from 2005. This second RSS will also be Taiwan's first indigenous satellite. Both the bus platform and remotesensing instrument (RSI) shall be designed and manufactured by NSPO and the Instrument Technology Research Center (ITRC) under the supervision of the National Applied Research Laboratories (NARL). Its onboard computer (OBC) shall use Taiwan's indigenous LEON-3 central processing unit (CPU). In order to achieve cost effective design, the commercial off the shelf (COTS) components shall be widely used. NSPO shall impose the up-screening/qualification and validation/verification processes to ensure their normal functions for proper operations in the severe space environments.

A new remotesensing system using the magneto-optical method is developed for inspection of flaws introduced during service operation where routine inspection is difficult because of difficult inaccessibility to the components. Among the advantages of non-destructive inspection (NDI) based on the magneto-optical sensor are: real time inspection, elimination of electrical noise and high spatial resolution. Remotesensing of flaws is achieved using the basic principles of Faraday effect, optical permeability, and diffraction of a laser by the domain walls. This paper describes a novel remote NDI system using the principles of optics and LMF. The main characteristic of the system is that image data and LMF information can be obtained simultaneously. It is possible to carry out remote and high speed inspection of cracks from the intensity of reflected light, and to estimate the size of a crack effectively with their diverse data. The advantages of this NDI system are demonstrated using two specimens. (author)

There has been an increase in the earth observation missions providing satellite imagery for operational monitoring applications. This technique has been found to be especially useful for the surveillance of large, remote areas, which is challenging to achieve in a cost-effective manner by conventional field-based or aerial means. This paper discussed the utility of satellite-based monitoring for different applications relevant to hydrology and water resources management. Emphasis was placed on the monitoring of river ice covers in near, real-time and water resources management. The paper first outlined river ice monitoring using remotesensing on the Lower Churchill River. The benefits of remotesensing over traditional survey methods for the dam industry was then outlined. Satellite image acquisition and interpretation for the Churchill River was then presented. Several images were offered. Watershed physiographic characterization using remotesensing was also described. It was concluded that satellite imagery proved to be a useful tool to develop physiographic characteristics when conducting rainfall-runoff modelling. 3 refs., 1 tab., 11 figs.

Introduction to RemoteSensing Principles and Concepts provides a comprehensive student introduction to both the theory and application of remotesensing. This textbook* introduces the field of remotesensing and traces its historical development and evolution* presents detailed explanations of core remotesensing principles and concepts providing the theory required for a clear understanding of remotelysensed images.* describes important remotesensing platforms - including Landsat, SPOT and NOAA * examines and illustrates many of the applications of remotelysensed images in various fields.

Full Text Available Evapotranspiration (ET is an essential part of the hydrological cycle and accurately estimating it plays a crucial role in water resource management. Surface energy balance (SEB models are widely used to estimate regional ET with remotesensing. The presence of horizontal advection, however, perturbs the surface energy balance system and contributes to the uncertainty of energy influxes. Thus, it is vital to consider horizontal advection when applying SEB models to estimate ET. This study proposes an innovative and simplified approach, the surface energy balance-advection (SEB-A method, which is based on the energy balance theory and also takes into account the horizontal advection to determine ET by remotesensing. The SEB-A method considers that the actual ET consists of two parts: the local ET that is regulated by the energy balance system and the exotic ET that arises from horizontal advection. To evaluate the SEB-A method, it was applied to the middle region of the Heihe River in China. Instantaneous ET for three days were acquired and assessed with ET measurements from eddy covariance (EC systems. The results demonstrated that the ET estimates had a high accuracy, with a correlation coefficient (R2 of 0.713, a mean average error (MAE of 39.3 W/m2 and a root mean square error (RMSE of 54.6 W/m2 between the estimates and corresponding measurements. Percent error was calculated to more rigorously assess the accuracy of these estimates, and it ranged from 0% to 35%, with over 80% of the locations within a 20% error. To better understand the SEB-A method, the relationship between the ET estimates and land use types was analyzed, and the results indicated that the ET estimates had spatial distributions that correlated with vegetation patterns and could well demonstrate the ET differences caused by different land use types. The sensitivity analysis suggested that the SEB-A method requested accurate estimation of the available energy, R n − G

Remotesensing for hazard response requires a priori identification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the RemoteSensing Communication...

The weather-related risks in crop production is not only crucial for farmers but also for market participants and policy makers since securing food supply is an important issue for society. While crop growth condition and phenology are essential information about such risks, the extensive observations on those are often non-existent in many parts of the world. In this study, we have developed a novel integrative approach to remotelysense crop growth condition and phenology at a large scale. For corn and soybeans in Iowa and Illinois of USA (2003-2014), we assessed crop growth condition and crop phenology by EO data and validated it against the United States Department of Agriculture (USDA) National Agriculture Statistics System (NASS) crop statistics. For growth condition, we used two distinguished approaches to acquire crop condition indicators: a process-based crop growth modelling and a satellite NDVI based method. Based on their pixel-wise historic distributions, we determined relative growth conditions and scaled-down to the state-level. For crop phenology, we calculated three crop phenology metrics [i.e., start of season (SOS), end of season (EOS), and peak of season (POS)] at the pixel level from MODIS 8-day Normalized Difference Vegetation Index (NDVI). The estimates were compared with the Crop Progress and Condition (CPC) data of NASS. For the condition, the state-level 10-day estimates showed a moderate agreement (RMSE 70%). Notably, the condition estimates corresponded to the severe soybeans disease in 2003 and the drought in 2012 for both crops. For the phenology, the average RMSE of the estimates was 8.6 day for the all three metrics. The average |ME| was smaller than 1.0 day after bias correction. The proposed method enables us to evaluate crop growth at any given period and place. Global climate changes are increasing the risk in agricultural production such as long-term drought. We hope that the presented remotesensingmethod for crop condition

Remotesensing techniques are quite dependable tools in investigating geologic problems, specially those related to structural aspects. The Landsat imagery provides discrimination between rock units, detection of large scale structures as folds and faults, as well as small scale fabric elements such as foliation and banding. In order to fulfill the aim of geologic application of remotesensing, some essential surveying maps might be done from images prior to the structural interpretation: land-use, land-form drainage pattern, lithological unit and structural lineament maps. Afterwards, the field verification should lead to interpretation of a comprehensive structural model of the study area to apply for the target problem. To deduce such a model, there are two ways of analysis the interpreter may go through: the direct and the indirect methods. The direct one is needed in cases where the resources or the targets are controlled by an obvious or exposed structural element or pattern. The indirect way is necessary for areas where the target is governed by a complicated structural pattern. Some case histories of structural modelling methods applied successfully for exploration of radioactive minerals, iron deposits and groundwater aquifers in Egypt are presented. The progress in imagery, enhancement and integration of remotesensing data with the other geophysical and geochemical data allow a geologic interpretation to be carried out which become better than that achieved with either of the individual data sets. 9 refs

The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remotesensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remotesensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remotesensing of plant pigments.

The current state of understanding of the biosphere is reviewed, the major scientific issues to be addressed are discussed, and techniques, existing and in need of development, for the science are evaluated. It is primarily concerned with developing the scientific capabilities of remotesensing for advancing the subject. The global nature of the scientific objectives requires the use of space-based techniques. The capability to look at the Earth as a whole was developed only recently. The space program has provided the technology to study the entire Earth from artificial satellites, and thus is a primary force in approaches to planetary biology. Space technology has also permitted comparative studies of planetary atmospheres and surfaces. These studies coupled with the growing awareness of the effects that life has on the entire Earth, are opening new lines of inquiry in science.

Remotesensing and measurements of the Moon from Apollo orbiting spacecraft and Earth form a basis for extrapolation of Apollo surface data to regions of the Moon where manned and unmanned spacecraft have not been and may be used to discover target regions for future lunar exploration which will produce the highest scientific yields. Orbital remotesensing and measurements discussed include (1) relative ages and inferred absolute ages, (2) gravity, (3) magnetism, (4) chemical composition, and (5) reflection of radar waves (bistatic). Earth-based remotesensing and measurements discussed include (1) reflection of sunlight, (2) reflection and scattering of radar waves, and (3) infrared eclipse temperatures. Photographs from the Apollo missions, Lunar Orbiters, and other sources provide a fundamental source of data on the geology and topography of the Moon and a basis for comparing, correlating, and testing the remotesensing and measurements. Relative ages obtained from crater statistics and then empirically correlated with absolute ages indicate that significant lunar volcanism continued to 2.5 b.y. (billion years) ago-some 600 m.y. (million years) after the youngest volcanic rocks sampled by Apollo-and that intensive bombardment of the Moon occurred in the interval of 3.84 to 3.9 b.y. ago. Estimated fluxes of crater-producing objects during the last 50 m.y. agree fairly well with fluxes measured by the Apollo passive seismic stations. Gravity measurements obtained by observing orbiting spacecraft reveal that mare basins have mass concentrations and that the volume of material ejected from the Orientale basin is near 2 to 5 million km 3 depending on whether there has or has not been isostatic compensation, little or none of which has occurred since 3.84 b.y. ago. Isostatic compensation may have occurred in some of the old large lunar basins, but more data are needed to prove it. Steady fields of remanent magnetism were detected by the Apollo 15 and 16 subsatellites

The nature of the U.S. energy problem is examined. Based upon the best available estimates, it appears that demand for OPEC oil will exceed OPEC productive capacity in the early to mid-eighties. The upward pressure on world oil prices resulting from this supply/demand gap could have serious international consequences, both financial and in terms of foreign policy implementation. National Energy Plan objectives in response to this situation are discussed. Major strategies for achieving these objectives include a conversion of industry and utilities from oil and gas to coal and other abundant fuels. Remotesensing from aircraft and spacecraft could make significant contributions to the solution of energy problems in a number of ways, related to exploration of energy-related resources, the efficiency and safety of exploitation procedures, power plant siting, environmental monitoring and assessment, and the transportation infrastructure.

The oceans cover over 70% of the earth's surface and the life inhabiting the oceans play an important role in shaping the earth's climate. Phytoplankton, the microscopic organisms in the surface ocean, are responsible for half of the photosynthesis on the planet. These organisms at the base of the food web take up light and carbon dioxide and fix carbon into biological structures releasing oxygen. Estimating the amount of microscopic phytoplankton and their associated primary productivity over the vast expanses of the ocean is extremely challenging from ships. However, as phytoplankton take up light for photosynthesis, they change the color of the surface ocean from blue to green. Such shifts in ocean color can be measured from sensors placed high above the sea on satellites or aircraft and is called "ocean color remotesensing." In open ocean waters, the ocean color is predominantly driven by the phytoplankton concentration and ocean color remotesensing has been used to estimate the amount of chlorophyll a, the primary light-absorbing pigment in all phytoplankton. For the last few decades, satellite data have been used to estimate large-scale patterns of chlorophyll and to model primary productivity across the global ocean from daily to interannual timescales. Such global estimates of chlorophyll and primary productivity have been integrated into climate models and illustrate the important feedbacks between ocean life and global climate processes. In coastal and estuarine systems, ocean color is significantly influenced by other light-absorbing and light-scattering components besides phytoplankton. New approaches have been developed to evaluate the ocean color in relationship to colored dissolved organic matter, suspended sediments, and even to characterize the bathymetry and composition of the seafloor in optically shallow waters. Ocean color measurements are increasingly being used for environmental monitoring of harmful algal blooms, critical coastal habitats

Understanding the sea floor biodiversity requires spatial information that can be acquired from remotesensing satellite data. Species volume, spatial patterns and species coverage are some of the information that can be derived. Current approaches for mapping sea bottom type have evolved from field observation, visual interpretation from aerial photography, mapping from remotesensing satellite data along with field survey and hydrograhic chart. Remotesensing offers most versatile technique to map sea bottom type up to a certain scale. This paper reviews the technical characteristics of signal and light interference within marine features, space and remotesensing satellite. In addition, related image processing techniques that are applicable to remotesensing satellite data for sea bottom type digital mapping is also presented. The sea bottom type can be differentiated by classification method using appropriate spectral bands of satellite data. In order to verify the existence of particular sea bottom type, field observations need to be carried out with proper technique and equipment

This report summarizes the technical work accomplished under Project THEMIS, A Center for RemoteSensing at the University of Kansas during the...period 16 September 1967 through 15 September 1973. The highlights of the four major areas forming the remotesensing system are presented. A detailed description of the latest radar spectrometer results is presented.

In the species rich tropics, forest conservation is often eclipsed by anthropogenic disturbance, resulting in a heightened need for an accurate assessment of biomass and the gaining of predictive capability before these ecosystems disappear. The combination of multi-temporal remotesensing data, field data and forest growth modeling to quantify carbon stocks and flux is therefore of great importance. In this study, we utilize these methods to (1) improve forest biomass and carbon flux estimates for the study region in Eastern Madagascar, and (2) initialize an individual-based growth model that incorporates the anthropogenic factors causing deforestation to project ecosystem response to future environmental change. Recent studies have shown that there is a direct correlation between the international rice market and rates of deforestation in tropical countries such as Madagascar (see Minten et al., 2006). Further, although law protects the remaining forest areas, dictatorships and recent political unrest have lead to poor or non-existent enforcement of precious wood and forest protection over the past 35 years. Our approach combined multi-temporal remotesensing analysis and ecological modeling using a theoretical and mathematical approach to assess biomass change and to understand how tree growth and life history (growth response patterns) relate to past and present economic variability in Madagascar forests of the eastern Toamasina region. We measured rates of change of deforestation with respect to politics and the price of rice by classifying and comparing biomass using 30m Landsat during 5 political regime time periods (1985-1992, 1993-1996, 1997-2001, 2002-2008, 2009 to present). Forest biomass estimations were calibrated using forest inventory data collected over 3 growing seasons over the study region (130 small circular plots in primary forest). This information was then built into the previously parameterized (Armstrong et al., in prep and Fischer et al in

Effectively Manage Wetland Resources Using the Best Available RemoteSensing Techniques Utilizing top scientists in the wetland classification and mapping field, RemoteSensing of Wetlands: Applications and Advances covers the rapidly changing landscape of wetlands and describes the latest advances in remotesensing that have taken place over the past 30 years for use in mapping wetlands. Factoring in the impact of climate change, as well as a growing demand on wetlands for agriculture, aquaculture, forestry, and development, this text considers the challenges that wetlands pose for remotesensing and provides a thorough introduction on the use of remotelysensed data for wetland detection. Taking advantage of the experiences of more than 50 contributing authors, the book describes a variety of techniques for mapping and classifying wetlands in a multitude of environments ranging from tropical to arctic wetlands including coral reefs and submerged aquatic vegetation. The authors discuss the advantages and di...

due to steep terrain, • phenological gradients across natural, agricultural and forestry ecosystems including plantations and • the need to serve the REDD-specific context of deforestation and forest degradation across spatial and temporal scales make remotesensing based approaches particularly...... be expected from remotesensing imagery and the provided information shall help to better anticipate problems that will be encountered when acquiring, analyzing and interpreting remotesensing data. Beyond remotesensing, it may be a good point of departure for a large group of scientists with a diverse...... and governance, and deforestation and forest degradation processes. The second part summarizes the available literature on remotesensing based good practices for REDD. It largely draws from the documents of the Intergovernmental Panel on Climate Change (IPCC), the United Nations Framework Convention on Climate...

The paper presents non-contact registration methods of the electromagnetic radiation which can be used for the detection of water pollution in rivers and water reservoirs. These methods include aerial photographs, satellite images and thermograms. The satellite images need reprocessing to obtain the mutual comparability of the images from various multispectral scanners (TM and MSS)

Full Text Available The rainfall and runoff relationship becomes an intriguing issue as urbanization continues to evolve worldwide. In this paper, we developed a simulation model based on the soil conservation service curve number (SCS-CN method to analyze the rainfall-runoff relationship in Guangzhou, a rapid growing metropolitan area in southern China. The SCS-CN method was initially developed by the Natural Resources Conservation Service (NRCS of the United States Department of Agriculture (USDA, and is one of the most enduring methods for estimating direct runoff volume in ungauged catchments. In this model, the curve number (CN is a key variable which is usually obtained by the look-up table of TR-55. Due to the limitations of TR-55 in characterizing complex urban environments and in classifying land use/cover types, the SCS-CN model cannot provide more detailed runoff information. Thus, this paper develops a method to calculate CN by using remotesensing variables, including vegetation, impervious surface, and soil (V-I-S. The specific objectives of this paper are: (1 To extract the V-I-S fraction images using Linear Spectral Mixture Analysis; (2 To obtain composite CN by incorporating vegetation types, soil types, and V-I-S fraction images; and (3 To simulate direct runoff under the scenarios with precipitation of 57mm (occurred once every five years by average and 81mm (occurred once every ten years. Our experiment shows that the proposed method is easy to use and can derive composite CN effectively.

Full Text Available In this paper, sequence ALOS PALSAR data and airborne SAR data of L-band from June 5, 2008 to September 8, 2015 are used. Based on the research of SAR data preprocessing and core algorithms, such as geocode, registration, filtering, unwrapping and baseline estimation, the improved Goldstein filtering algorithm and the branch-cut path tracking algorithm are used to unwrap the phase. The DEM and surface deformation information of the experimental area were extracted. Combining SAR-specific geometry and differential interferometry, on the basis of composite analysis of multi-source images, a method of detecting landslide disaster combining coherence of SAR image is developed, which makes up for the deficiency of single SAR and optical remotesensing acquisition ability. Especially in bad weather and abnormal climate areas, the speed of disaster emergency and the accuracy of extraction are improved. It is found that the deformation in this area is greatly affected by faults, and there is a tendency of uplift in the southeast plain and western mountainous area, while in the southwest part of the mountain area there is a tendency to sink. This research result provides a basis for decision-making for local disaster prevention and control.

Full Text Available An automatic urban building extraction method for high-resolution remote-sensing imagery,which combines building segmentation based on neighbor total variations with object-oriented analysis,is presented in this paper. Aimed at different extraction complexity from various buildings in the segmented image,a hierarchical building extraction strategy with multi-feature fusion is adopted. Firstly,we extract some rectangle buildings which remain intact after segmentation through shape analysis. Secondly,in order to ensure each candidate building target to be independent,multidirectional morphological road-filtering algorithm is designed which can separate buildings from the neighboring roads with similar spectrum. Finally,we take the extracted buildings and the excluded non-buildings as samples to establish probability model respectively,and Bayesian discriminating classifier is used for making judgment of the other candidate building objects to get the ultimate extraction result. The experimental results have shown that the approach is able to detect buildings with different structure and spectral features in the same image. The results of performance evaluation also support the robustness and precision of the approach developed.

The aim of this paper is to investigate if the incorporation of the uncertainty associated with the classification of surface elements into the classification of landscape units (LUs) increases the results accuracy. To this end, a hybrid classification method is developed, including uncertainty information in the classification of very high spatial resolution multi-spectral satellite images, to obtain a map of LUs. The developed classification methodology includes the following...

Recent research in range ecology has emphasized the importance of forage quality as a key indicator of rangeland condition. However, we lack tools to evaluate forage quality at scales appropriate for management. Using canopy reflectance data to measure forage quality has been conducted at both laboratory and field levels separately, but little work has been conducted to evaluate these methods simultaneously. The objective of this study is to find a reliable way of assessing grassland quality ...

Full Text Available Spatially varying haze is a common feature of most satellite images currently used for land cover classification and mapping and can significantly affect image quality. In this paper, we present a high-fidelity haze removal method based on Haze Optimized Transformation (HOT, comprising of three steps: semi-automatic HOT transform, HOT perfection and percentile based dark object subtraction (DOS. Since digital numbers (DNs of band red and blue are highly correlated in clear sky, the R-squared criterion is utilized to search the relative clearest regions of the whole scene automatically. After HOT transform, spurious HOT responses are first masked out and filled by means of four-direction scan and dynamic interpolation, and then homomorphic filter is performed to compensate for loss of HOT of masked-out regions with large areas. To avoid patches and halo artifacts, a procedure called percentile DOS is implemented to eliminate the influence of haze. Scenes including various land cover types are selected to validate the proposed method, and a comparison analysis with HOT and Background Suppressed Haze Thickness Index (BSHTI is performed. Three quality assessment indicators are selected to evaluate the haze removed effect on image quality from different perspective and band profiles are utilized to analyze the spectral consistency. Experiment results verify the effectiveness of the proposed method for haze removal and the superiority of it in preserving the natural color of object itself, enhancing local contrast, and maintaining structural information of original image.

Recent research in range ecology has emphasized the importance of forage quality as a key indicator of rangeland condition. However, we lack tools to evaluate forage quality at scales appropriate for management. Using canopy reflectance data to measure forage quality has been conducted at both laboratory and field levels separately, but little work has been conducted to evaluate these methods simultaneously. The objective of this study is to find a reliable way of assessing grassland quality through measuring forage chemistry with reflectance. We studied a mixed grass ecosystem in Grasslands National Park of Canada and surrounding pastures, located in southern Saskatchewan. Spectral reflectance was collected at both in-situ field level and in the laboratory. Vegetation samples were collected at each site, sorted into the green grass portion, and then sent to a chemical company for measuring forage quality variables, including protein, lignin, ash, moisture at 135 °C, Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF), Total Digestible, Digestible Energy, Net Energy for Lactation, Net Energy for Maintenance, and Net Energy for Gain. Reflectance data were processed with the first derivative transformation and continuum removal method. Correlation analysis was conducted on spectral and forage quality variables. A regression model was further built to investigate the possibility of using canopy spectral measurements to predict the grassland quality. Results indicated that field level prediction of protein of mixed grass species was possible (r² = 0.63). However, the relationship between canopy reflectance and the other forage quality variables was not strong.

Full Text Available Recent research in range ecology has emphasized the importance of forage quality as a key indicator of rangeland condition. However, we lack tools to evaluate forage quality at scales appropriate for management. Using canopy reflectance data to measure forage quality has been conducted at both laboratory and field levels separately, but little work has been conducted to evaluate these methods simultaneously. The objective of this study is to find a reliable way of assessing grassland quality through measuring forage chemistry with reflectance. We studied a mixed grass ecosystem in Grasslands National Park of Canada and surrounding pastures, located in southern Saskatchewan. Spectral reflectance was collected at both in-situ field level and in the laboratory. Vegetation samples were collected at each site, sorted into the green grass portion, and then sent to a chemical company for measuring forage quality variables, including protein, lignin, ash, moisture at 135 ºC, Neutral Detergent Fiber (NDF, Acid Detergent Fiber (ADF, Total Digestible, Digestible Energy, Net Energy for Lactation, Net Energy for Maintenance, and Net Energy for Gain. Reflectance data were processed with the first derivative transformation and continuum removal method. Correlation analysis was conducted on spectral and forage quality variables. A regression model was further built to investigate the possibility of using canopy spectral measurements to predict the grassland quality. Results indicated that field level prediction of protein of mixed grass species was possible (r2 = 0.63. However, the relationship between canopy reflectance and the other forage quality variables was not strong.

Built-up area marks the use of city construction land in the different periods of the development, the accurate extraction is the key to the studies of the changes of urban expansion. This paper studies the technology of automatic extraction of urban built-up area based on object-oriented method and remotesensing data, and realizes the automatic extraction of the main built-up area of the city, which saves the manpower cost greatly. First, the extraction of construction land based on object-oriented method, the main technical steps include: (1) Multi-resolution segmentation; (2) Feature Construction and Selection; (3) Information Extraction of Construction Land Based on Rule Set, The characteristic parameters used in the rule set mainly include the mean of the red band (Mean R), Normalized Difference Vegetation Index (NDVI), Ratio of residential index (RRI), Blue band mean (Mean B), Through the combination of the above characteristic parameters, the construction site information can be extracted. Based on the degree of adaptability, distance and area of the object domain, the urban built-up area can be quickly and accurately defined from the construction land information without depending on other data and expert knowledge to achieve the automatic extraction of the urban built-up area. In this paper, Beijing city as an experimental area for the technical methods of the experiment, the results show that: the city built-up area to achieve automatic extraction, boundary accuracy of 2359.65 m to meet the requirements. The automatic extraction of urban built-up area has strong practicality and can be applied to the monitoring of the change of the main built-up area of city.

a TLS point cloud. We validated the mapping with field observations and high resolution digital photographs. TLS provides 3D data to precisely characterize the morphology of vertical and overhanging rock faces. With the recently developed methods it is possible to remotely map geologic limits and exfoliation joints, as well as to assess the density of potential failure mechanisms directly on the TLS point clouds. These advances in remotesensingmethods provide new tools to locate the most probable future rockfall sources and provide key elements needed to evaluate the potential rockfall hazard of every area of the cliffs in Yosemite Valley.

Evapotranspiration (ET) is an important component of the water and energy cycle. The present study develops a practical approach for generating all-sky ET with the synergistic use of satellite images and meteorological data. In this approach, the ET over clear-sky pixels is estimated from a two-stage land surface temperature (LST)/fractional vegetation cover feature space method where the dry/wet edges are determined from theoretical calculations. For cloudy pixels, the Penman-Monteith equation is used to calculate the ET where no valid remotelysensed LST is available. An evaluation of the method with ET collected at ground-based large aperture scintillometer measurements at the Yucheng Comprehensive Experimental Station (YCES) in China is performed over a growth period from April to October 2010. The results show that the root-mean-square error (RMSE) and bias over clear-sky pixels are 57.3 W/m2 and 18.2 W/m2, respectively, whereas an RMSE of 69.3 W/m2 with a bias of 12.3 W/m2 can be found over cloudy pixels. Moreover, a reasonable overall RMSE of 65.3 W/m2 with a bias of 14.4 W/m2 at the YCES can be obtained under all-sky conditions, indicating a promising prospect for the derivation of all-sky ET using currently available satellite and meteorological data at a regional or global scale in future developments.

Full Text Available The Special Issue (SI on “RemoteSensing in Coastal Environments” presents a wide range of articles focusing on a variety of remotesensing models and techniques to address coastal issues and processes ranging for wetlands and water quality to coral reefs and kelp habitats. The SI is comprised of twenty-one papers, covering a broad range of research topics that employ remotesensing imagery, models, and techniques to monitor water quality, vegetation, habitat suitability, and geomorphology in the coastal zone. This preface provides a brief summary of each article published in the SI.

Full Text Available Focused on the issue that conventional remotesensing image classification methods have run into the bottlenecks in accuracy, a new remotesensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised Greedy layer-wise training algorithm is used to train each layer in turn for more robust expressing, characteristics are obtained in supervised learning by Back Propagation (BP neural network, and the whole network is optimized by error back propagation. Finally, Gaofen-1 satellite (GF-1 remotesensing data are used for evaluation, and the total accuracy and kappa accuracy reach 95.7% and 0.955, respectively, which are higher than that of the Support Vector Machine and Back Propagation neural network. The experiment results show that the proposed method can effectively improve the accuracy of remotesensing image classification.

Microwave remotesensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive microwave soil moisture sensors currently considered for space operation are in the range 10–20 km. The most useful frequency range for soil moisture sensing is 1–5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods. Particularly promising are methods for optimally assimilating passive microwave data into hydrologic models. Further studies are needed to investigate the effects on microwave observations of within-footprint spatial heterogeneity of vegetation cover and subsurface soil characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale soil moisture information from remote observations

The National Satellite Land RemoteSensing Data Archive (NSLRSDA) resides at the U.S. Geological Survey's (USGS) Earth Resources Observation and Science (EROS) Center. Through the Land RemoteSensing Policy Act of 1992, the U.S. Congress directed the Department of the Interior (DOI) to establish a permanent Government archive containing satellite remotesensing data of the Earth's land surface and to make this data easily accessible and readily available. This unique DOI/USGS archive provides a comprehensive, permanent, and impartial observational record of the planet's land surface obtained throughout more than five decades of satellite remotesensing. Satellite-derived data and information products are primary sources used to detect and understand changes such as deforestation, desertification, agricultural crop vigor, water quality, invasive plant species, and certain natural hazards such as flood extent and wildfire scars.

Including an introduction and historical overview of the field, this comprehensive synthesis of the major biophysical applications of satellite remotesensing includes in-depth discussion of satellite-sourced biophysical metrics such as leaf area index.

National Oceanic and Atmospheric Administration, Department of Commerce — The RemoteSensing Division is responsible for providing data to support the Coastal Mapping Program, Emergency Response efforts, and the Aeronautical Survey Program...

The Department of Energy has established a program called Comprehensive, Integrated RemoteSensing (CIRS). The overall objective of the program is to provide a state-of-the-art data base of remotelysensed data for all users of such information at large DOE sites. The primary types of remotesensing provided, at present, consist of the following: large format aerial photography, video from aerial platforms, multispectral scanning, and airborne nuclear radiometric surveys. Implementation of the CIRS Program by EG and G Energy Measurements, Inc. began with field operations at the Savannah River Plant in 1982 and is continuing at that DOE site at a level of effort of about $1.5 m per year. Integrated remotesensing studies were subsequently extended to the West Valley Demonstration Project in this summer and fall of 1984. It is expected that the Program will eventually be extended to cover all large DOE sites on a continuing basis

Aircraft and satellite aerial photographs represent indispensible tools for environmental observation today. They contribute to a systematic inventory of important environmental parameters such as climate, vegetation or surface water. Their great importance lies in the continuous monitoring of large regions so that changes in environmental conditions are quickly detected. This book provides an overview of the capabilities of remotesensing in environmental monitoring and in the recognition of environmental problems as well as of the usefulness of remotesensing data for environmental planning. Also addressed is the role of remotesensing in the monitoring of natural hazards such as earthquakes and volcano eruptions as well as problems of remotesensing technology transfer to developing countries. (orig.) [de

Full Text Available industries. In this paper we introduce the results from a remotesensing campaign performed in September 2001 at night time. For the first time nocturnal light pollution was measured at high spatial and spectral resolution using two airborne hyperspectral sensors, namely the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS and the Visible InfraRed Scanner (VIRS-200. These imagers, generally employed for day-time Earth remotesensing, were flown over the Tuscany coast (Italy on board of a Casa 212/200 airplane from an altitude of 1.5-2.0 km. We describe the experimental activities which preceded the remotesensing campaign, the optimization of sensor configuration, and the images as far acquired. The obtained results point out the novelty of the performed measurements and highlight the need to employ advanced remotesensing techniques as a spectroscopic tool for light pollution monitoring.

Full text: Basic principles of remotesensing of environment are outlined emphasizing inherent physical and target properties leading to proper identification and classification. Basic processing techniques are discussed. Applications of remotesensing techniques in various aspects of environmental monitoring and assessment is surveyed with emphasis on aspects of main concern to developing communities such as planning, sea level impacts, mine detection and earthquake prediction are all outlined and discussed

Full Text Available Education in remotesensing and GIS is based on software utilization. The software needs to be installed in computer rooms with a certain number of licenses. The commercial software equipment is therefore financially demanding and not only for universities, but especially for students. Internet research brings a long list of free software of various capabilities. The paper shows a present state of GIS, image processing and remotesensing free software.

A set of operators of remotesensing applications have been proposed to fulfill most of the Functional Requirements (FR). These operators capture the functions of the applications, which can be considered as the services provided by the applications. In general, a good application meets maximum FR from user. In this paper, we have defined a remotesensing application by a set, having all images created at dissimilar time instances, and each image is categorized into set of different layers. (author)

Education in remotesensing and GIS is based on software utilization. The software needs to be installed in computer rooms with a certain number of licenses. The commercial software equipment is therefore financially demanding and not only for universities, but especially for students. Internet research brings a long list of free software of various capabilities. The paper shows a present state of GIS, image processing and remotesensing free software.

Full Text Available Estimation of actual evapotranspiration (ETa based on remotelysensed imagery is very valuable in agricultural regions where ETa rates can vary greatly from field to field. This research utilizes the image processing model METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration to estimate late season, post-harvest ETa rates from fields with a cover crop planted after a cash crop (in this case, a rye/radish/pea mixture planted after spring wheat. Remotelysensed EToF (unit-less fraction of grass-based reference ET, ETo maps were generated using Erdas Imagine software for a 260 km2 area in northeastern South Dakota, USA. Meteorological information was obtained from a Bowen-Ratio Energy Balance System (BREBS located within the image. Nine image dates were used for the growing season, from May through October. Five of those nine were captured during the cover crop season. METRIC was found to successfully differentiate between fields with and without cover crops. In a blind comparison, METRIC compared favorably with the estimated ETa rates found using the BREBS (ETλE, with a difference in total estimated ETa for the cover crop season of 7%.

I. RemoteSensing Basics A. The electromagnetic spectrum demonstrates what we can see both in the visible and beyond the visible part of the spectrum through the use of various types of sensors. B. Resolution refers to what a remote sensor can see and how often. 1. Sp...

RemoteSensing Digital Image Analysis provides the non-specialist with a treatment of the quantitative analysis of satellite and aircraft derived remotely sensed data. Since the first edition of the book there have been significant developments in the algorithms used for the processing and analysis of remotesensing imagery; nevertheless many of the fundamentals have substantially remained the same. This new edition presents material that has retained value since those early days, along with new techniques that can be incorporated into an operational framework for the analysis of remotesensing data. The book is designed as a teaching text for the senior undergraduate and postgraduate student, and as a fundamental treatment for those engaged in research using digital image processing in remotesensing. The presentation level is for the mathematical non-specialist. Since the very great number of operational users of remotesensing come from the earth sciences communities, the text is pitched at a leve...

Full Text Available Automated detection of landscape patterns on RemoteSensing imagery has seen virtually little or no development in the archaeological domain, notwithstanding the fact that large portion of cultural landscapes worldwide are characterized by land engineering applications. The current extraordinary availability of remotelysensed images makes it now urgent to envision and develop automatic methods that can simplify their inspection and the extraction of relevant information from them, as the quantity of information is no longer manageable by traditional “human” visual interpretation. This paper expands on the development of automatic methods for the detection of target landscape features—represented by field system patterns—in very high spatial resolution images, within the framework of an archaeological project focused on the landscape engineering embedded in Roman cadasters. The targets of interest consist of a variety of similarly oriented objects of diverse nature (such as roads, drainage channels, etc. concurring to demark the current landscape organization, which reflects the one imposed by Romans over two millennia ago. The proposed workflow exploits the textural and shape properties of real-world elements forming the field patterns using multiscale analysis of dominant oriented response filters. Trials showed that this approach provides accurate localization of target linear objects and alignments signaled by a wide range of physical entities with very different characteristics.

Full Text Available For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remotesensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing, cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry, and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures. We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remotesensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

Harmful Algal Blooms (HABs) in the Gulf of Mexico (GOM) are natural phenomena that can have negative impacts on marine ecosystems on which human health and the economy of some Gulf States depends. Many of the HABs in the GOM are dominated by the toxic dinoflagellate Karenia brevis. Non-toxic phytoplankton taxa such as Scrippsiella sp. also form intense blooms off the Mexican coast that result in massive fish mortality and economic losses, particularly as they may lead to anoxia. The main objectives of this dissertation were to (1) evaluate and improve the techniques developed for detection of Karenia spp. blooms on the West Florida Shelf (WFS) using satellite remotesensingmethods, (2) test the use of these methods for waters in the GOM, and (3) use the output of these techniques to better understand the dynamics and evolution of Karenia spp. blooms in the WFS and off Mexico. The first chapter of this dissertation examines the performance of several Karenia HABs detection techniques using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images and historical ground truth observations collected on the WFS from August 2002 to December 2011. A total of 2323 in situ samples collected by the Florida Fish and Wildlife Research Institute to test for Karenia spp. matched pixels with valid ocean color satellite observations over this period. This dataset was used to systematically optimize variables and coefficients used in five published HAB detection methods. Each technique was tested using a set of metrics that included the F-Measure (FM). Before optimization, the average FM for all techniques was 0.47. After optimization, the average FM increased to 0.59, and false positives decreased ~50%. The addition of a Fluorescence Line Height (FLH) criterion improved the performance of every method. A new practical method was developed using a combination of FLH and RemoteSensing Reflectance at 555 nm (Rrs555-FLH). The new method resulted in an FM of 0.62 and 3

Remotesensing has steadily become an integral part of multiple disciplines, research, and education. Remotesensing can be defined as the process of acquiring information about an object or area of interest without physical contact. As remotesensing becomes a necessity in solving real world problems and scientific questions an important question to consider is why remotesensing training is significant to education and is it relevant to training students in this discipline. What has been discovered is the interest in Science, Technology, Engineering and Mathematics (STEM) fields, specifically remotesensing, has declined in our youth. The Center of Excellence in RemoteSensing Education and Research (CERSER) continuously strives to provide education and research opportunities on ice sheet, coastal, ocean, and marine science. One of those continued outreach efforts are Center for RemoteSensing of Ice Sheets (CReSIS) Middle School Program. Sponsored by the National Science Foundation CReSIS Middle School Program offers hands on experience for middle school students. CERSER and NSF offer students the opportunity to study and learn about remotesensing and its vital role in today's society as it relate to climate change and real world problems. The CReSIS Middle School Program is an annual two-week effort that offers middle school students experience with remotesensing and its applications. Specifically, participants received training with Global Positioning Systems (GPS) where the students learned the tools, mechanisms, and applications of a Garmin 60 GPS. As a part of the program the students were required to complete a fieldwork assignment where several longitude and latitude points were given throughout campus. The students had to then enter the longitude and latitude points into the Garmin 60 GPS, navigate their way to each location while also accurately reading the GPS to make sure travel was in the right direction. Upon completion of GPS training the

RemoteSensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remotesensing techniques, design and applications published in RemoteSensing. We are pleased to announce the first “RemoteSensing Best Paper Award” for the year 2014.

A study of the role of remotesensing for geologic reconnaissance for tunnel-site selection was commenced. For this study, remotesensing was defined...conventional remotesensing . Future research directions are suggested, and the extension of remotesensing to include airborne passive microwave

The most common form of remotesensing as applied to oil spills is aerial remotesensing. The technology of aerial remotesensing, mainly from aircraft, is reviewed along with aircraft-mounted remote sensors and aircraft modifications. The characteristics, advantages, and limitations of optical techniques, infrared and ultraviolet sensors, fluorosensors, microwave and radar sensors, and slick thickness sensors are discussed. Special attention is paid to remotesensing of oil under difficult circumstances, such as oil in water or oil on ice. An infrared camera is the first sensor recommended for oil spill work, as it is the cheapest and most applicable device, and is the only type of equipment that can be bought off-the-shelf. The second sensor recommended is an ultraviolet and visible-spectrum device. The laser fluorosensor offers the only potential for discriminating between oiled and un-oiled weeds or shoreline, and for positively identifying oil pollution on ice and in a variety of other situations. However, such an instrument is large and expensive. Radar, although low in priority for purchase, offers the only potential for large-area searches and foul-weather remotesensing. Most other sensors are experimental or do not offer good potential for oil detection or mapping. 48 refs., 8 tabs

Remotelysensed indices of burn severity are now commonly used by researchers and land managers to assess fire effects, but their relationship to field-based assessments of burn severity has been evaluated only in a few ecosystems. This analysis illustrates two cases in which methodological refinements to field-based and remotelysensed indices of burn severity...

This slide presentation reviews current NASA Earth RemoteSensing observations in specific reference to improving public health information in view of pollen sensing. While pollen sampling has instrumentation, there are limitations, such as lack of stations, and reporting lag time. Therefore it is desirable use remotesensing to act as early warning system for public health reasons. The use of Juniper Pollen was chosen to test the possibility of using MODIS data and a dust transport model, Dust REgional Atmospheric Model (DREAM) to act as an early warning system.

Remotesensing uses a wide variety of techniques and methods. Resulting data are analyzed by man and machine, using both analog and digital technology. The newest and most important initiatives in the U. S. civilian space program currently revolve around the space station complex, which includes the core station as well as co-orbiting and polar satellite platforms. This proposed suite of platforms and support systems offers a unique potential for facilitating long term, multidisciplinary scientific investigations on a truly global scale. Unlike previous generations of satellites, designed for relatively limited constituencies, the space station offers the potential to provide an integrated source of information which recognizes the scientific interest in investigating the dynamic coupling between the oceans, land surface, and atmosphere. Earth scientist already face problems that are truly global in extent. Problems such as the global carbon balance, regional deforestation, and desertification require new approaches, which combine multidisciplinary, multinational research teams, employing advanced technologies to produce a type, quantity, and quality of data not previously available. The challenge before the international scientific community is to continue to develop both the infrastructure and expertise to, on the one hand, develop the science and technology of remotesensing, while on the other hand, develop an integrated understanding of global life support systems, and work toward a quantiative science of the biosphere.

Full Text Available Learning incorporates a broad range of complex procedures. Machine learning (ML is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc. that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remotesensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remotesensing problems.

Parking is an integral part of the traffic system everywhere. Provision of parking facilities to meet peak of demands parking in cities of millions is always a real challenge for traffic and transport experts. Parking demand is a function of population and car ownership which is obtained from traffic statistics. Parking supply in an area is the number of legal parking stalls available in that area. The traditional treatment of the parking studies utilizes data collected either directly from on street counting and inquiries or indirectly from local and national traffic censuses. Both methods consume time, efforts, and funds. Alternatively, it is reasonable to make use of the eventually available data based on remotelysensed data which might be flown for other purposes. The objective of this work is to develop a new approach based on utilization of integration of remotelysensed data, field measurements, censuses and traffic records of the studied area for studying domestic parking problems in residential areas especially in informal areas. Expected outcomes from the research project establish a methodology to manage the issue and to find the reasons caused the shortage in domestics and the solutions to overcome this problems.

The remotesensing of urban areas has received much attention from scientists conducting studies on measuring sprawl, congestion, pollution, poverty, and environmental encroachment. Yet much of the research is case and data-specific where results are greatly influenced by prevailing local conditions. There seems to be a lack of epistemological links between remotesensing and conventional theoretical urban geography; in other words, an oversight for the appreciation of how urban theory fuels urban change and how urban change is measured by remotelysensed data. This paper explores basic urban theories such as centrality, mobility, materiality, nature, public space, consumption, segregation and exclusion, and how they can be measured by remotesensing sources. In particular, the link between structure (tangible objects) and function (intangible or immaterial behavior) is addressed as the theory that supports the wellknow contrast between land cover and land use classification from remotelysensed data. The paper then couches these urban theories and contributions from urban remotesensing within two analytical fields. The first is the search for an "appropriate" spatial scale of analysis, which is conveniently divided between micro and macro urban remotesensing for measuring urban structure, understanding urban processes, and perhaps contributions to urban theory at a variety of scales of analysis. The second is on the existence of a temporal lag between materiality of urban objects and the planning process that approved their construction, specifically how time-dependence in urban structural-functional models produce temporal lags that alter the causal links between societal and political functional demands and structural ramifications.

This paper has discussed the latest development of satellite remotesensing in sensor resolutions, satellite motion models, load forms, data processing and its application. The authors consider that sensor resolutions of satellite remotesensing have increased largely. Valid integration of multisensors is a new idea and technology of satellite remotesensing in the 21st century, and post-remotesensing application technology is the important part of deeply applying remotesensing information and has great practical significance. (authors)

Several remotesensing software packages are used to the explicit purpose of analyzing and visualizing remotelysensed data, with the developing of remotesensing sensor technologies from last ten years. Accord-ing to literature, the remotesensing is still the lack of software tools for effective information extraction from remotesensing data. So, this paper provides a state-of-art of multi-sensor image fusion technologies as well as review on the quality evaluation of the single image or f...

Full Text Available There does already exist a wide variety of tutorials and on-line courses on Photogrammetry and RemoteSensing very often used in academia. Many of them are still rather static and tedious or target high-knowledge learners. E-learning is, however, increasingly applied by many organizations and companies for life-long learning (like e.g. the EduServ courses of EuroSDR, but also for training of resellers and in order to save the expenses and time of travelling. A new issue of this project when taking into account the ethnic mentality in some countries like Saudi Arabia where it is impossible to mix the females and males at any institution type or for instance to teach ladies by a male teacher face to face, many academic workshops have been done separately twice by foreign organizations to adapt this situation. This paper will focus on these issues and present experiences gathered from a Master Thesis on "E-learning in Digital Photogrammetry and RemoteSensing for Non Experts using Moodle" at HFT Stuttgart in co-operation with a software vendor and a reseller and experiences from a current European Tempus IV project GIDEC (Geographic information technology for sustainable development in Eastern neighouring countries. The aim of this research is to provide an overview on available methods and tools and classify and judge their feasibility for the above mentioned scenarios. A more detailed description is given on the development of e-learning applications for Digital Photogrammetry and RemoteSensing using the open source package Moodle as platform. A first item covers the experiences from setting up and handling of Moodle for non-experts. The major emphasis is then on developing and analyzing some few case studies for lectures, exercises, and software training in the fields of Digital Photogrammetry and RemoteSensing. Feedback from students and company staff will be evaluated and incorporated in an improved design and sample implementation. A

Emissions of nitrogen oxides (NOx) by vehicles in real driving environments are only partially understood. This has been brought to the attention of the world with recent revelations of the cheating of the type of approval tests exposed in the dieselgate scandal. Remote-sensing devices offer investigators an opportunity to directly measure in situ real driving emissions of tens of thousands of vehicles. Remote-sensing NO 2 measurements are not as widely available as would be desirable. The aim of this study is to improve the ability of investigators to estimate the NO 2 emissions and to improve the confidence of the total NOx results calculated from standard remote-sensing device (RSD) measurements. The accuracy of the RSD speed and acceleration module was also validated using state-of-the-art onboard global positioning system (GPS) tracking. Two RSDs used in roadside vehicle emissions surveys were tested side by side under off-carriageway conditions away from transient pollution sources to ascertain the consistency of their measurements. The speed correlation was consistent across the range of measurements at 95% confidence and the acceleration correlation was consistent at 95% confidence intervals for all but the most extreme acceleration cases. VSP was consistent at 95% confidence across all measurements except for those at VSP ≥ 15 kW t -1 , which show a small underestimate. The controlled distribution gas nitric oxide measurements follow a normal distribution with 2σ equal to 18.9% of the mean, compared to 15% observed during factory calibration indicative of additional error introduced into the system. Systematic errors of +84 ppm were observed but within the tolerance of the control gas. Interinstrument correlation was performed, with the relationship between the FEAT and the RSD4600 being linear with a gradient of 0.93 and an R 2 of 0.85, indicating good correlation. A new method to calculate NOx emissions using fractional NO 2 combined with NO

There does already exist a wide variety of tutorials and on-line courses on Photogrammetry and RemoteSensing very often used in academia. Many of them are still rather static and tedious or target high-knowledge learners. E-learning is, however, increasingly applied by many organizations and companies for life-long learning (like e.g. the EduServ courses of EuroSDR), but also for training of resellers and in order to save the expenses and time of travelling. A new issue of this project when taking into account the ethnic mentality in some countries like Saudi Arabia where it is impossible to mix the females and males at any institution type or for instance to teach ladies by a male teacher face to face, many academic workshops have been done separately twice by foreign organizations to adapt this situation. This paper will focus on these issues and present experiences gathered from a Master Thesis on "E-learning in Digital Photogrammetry and RemoteSensing for Non Experts using Moodle" at HFT Stuttgart in co-operation with a software vendor and a reseller and experiences from a current European Tempus IV project GIDEC (Geographic information technology for sustainable development in Eastern neighouring countries). The aim of this research is to provide an overview on available methods and tools and classify and judge their feasibility for the above mentioned scenarios. A more detailed description is given on the development of e-learning applications for Digital Photogrammetry and RemoteSensing using the open source package Moodle as platform. A first item covers the experiences from setting up and handling of Moodle for non-experts. The major emphasis is then on developing and analyzing some few case studies for lectures, exercises, and software training in the fields of Digital Photogrammetry and RemoteSensing. Feedback from students and company staff will be evaluated and incorporated in an improved design and sample implementation. A further focus is on free

Full Text Available Remotesensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC. Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remotesensing of EBC from the perspective of remotesensing specialists, i.e., it is organized in the context of state-of-the-art remotesensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI, inversion algorithm, data fusion, and the integration of remotesensing (RS and geographic information system (GIS.

Remotesensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remotesensing of EBC from the perspective of remotesensing specialists, i.e., it is organized in the context of state-of-the-art remotesensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remotesensing (RS) and geographic information system (GIS).

Full Text Available Earth and its environment are studied by different scientific disciplines as geosciences, science of engineering, social sciences, geography, etc. The study of the above, beyond pure scientific interest, is useful for the practical needs of man. Photogrammetry and RemoteSensing (defined by Statute II of ISPRS is the art, science, and technology of obtaining reliable information from non-contact imaging and other sensor systems about the Earth and its environment, and other physical objects and of processes through recording, measuring, analyzing and representation. Therefore, according to this definition, photogrammetry and remotesensing can support studies of the above disciplines for acquisition of geoinformation. This paper concerns basic concepts of geosciences (geomorphology, geology, hydrology etc, and the fundamentals of photogrammetry-remotesensing, in order to aid the understanding of the relationship between photogrammetry-remotesensing and geoinformation and also structure curriculum in a brief, concise and coherent way. This curriculum can represent an appropriate research and educational outline and help to disseminate knowledge in various directions and levels. It resulted from our research and educational experience in graduate and post-graduate level (post-graduate studies relative to the protection of environment and protection of monuments and historical centers in the Lab. of Photogrammetry – RemoteSensing in Civil Engineering Faculty of Aristotle University of Thessaloniki.

the entire network of more than 1100 miles of levees in the area, has used several sets of in situ data to validate the results. This type of levee health status information acquired with radar remotesensing could provide a cost-effective method to significantly improve the spatial and temporal coverage of levee systems and identify areas of concern for targeted levee maintenance, repair, and emergency response in the future. Our results show, for example, that during an emergency, when time is of the essence, SAR remotesensing offers the potential of rapidly providing levee status information that is effectively impossible to obtain over large areas using conventional monitoring, e.g., through high precision measurements of subcentimeter-scale levee movement prior to failure. The research described here was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

Abstract The study investigated diurnal and seasonal dynamics of evapotranspiration (ET) and transpiration (Tr) in a soybean canopy, as well as the relationships among ET, Tr, solar radiation and remotelysensed spectral reflectance. The eddy covariance method (ECM) and stem heat balance method (SHBM) were used for independent measurement of ET and Tr, respectively. Micrometeorological, soil, and spectral reflectance data were acquired for the entire growing season. The instantaneous values of canopy-Tr estimated by SHBM and ET by ECM were well synchronized with each other, and both were strongly affected by the solar radiation. The daily values canopy-Tr increased rapidly with increasing leaf area index (LAI), and got closer to the ET even at a low value of LAI such as 1.5-2. The daily values of ET were moderately correlated with global solar radiation (Rs), and more closely with the potential evapotranspiration (ETp), estimated by the 'radiation method.' This fact supported the effectiveness of the simple radiation method in estimation of evapotranspiration. The ratio of Tr/ET as well as the ratio of ground heat flux (G) to Rs (G/Rs) was closely related to LAI, and LAI was a key variable in determining the energy partitioning to soil and vegetation. It was clearly shown that a remotelysensed vegetation index such as SAVI (soil adjusted vegetation index) was effective for estimating LAI, and further useful for directly estimating energy partitioning to soil and vegetation. The G and Tr/ET were both well estimated by the vegetation index. It was concluded that the combination of a simple radiation method with remotelysensed information can provide useful information on energy partitioning and Tr/ET in vegetation canopies

RemoteSensing Applications in Environmental Research is the basis for advanced Earth Observation (EO) datasets used in environmental monitoring and research. Now that there are a number of satellites in orbit, EO has become imperative in today's sciences, weather and natural disaster prediction. This highly interdisciplinary reference work brings together diverse studies on remotesensing and GIS, from a theoretical background to its applications, represented through various case studies and the findings of new models. The book offers a comprehensive range of contributions by well-known scientists from around the world and opens a new window for students in presenting interdisciplinary and methodological resources on the latest research. It explores various key aspects and offers state-of-the-art research in a simplified form, describing remotesensing and GIS studies for those who are new to the field, as well as for established researchers.

Full Text Available Remotesensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remotesensing images with different viewpoint, which further increases the difficulty of remotesensing image registration. To address the problem, we propose a multi-viewpoint remotesensing image registration method which contains the following contributions. (i A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform distance which is endowed with the intensity information is used to measure the scale space extrema. (iii To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remotesensing images obtained from the unmanned aerial vehicle (UAV and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

A method and apparatus for remotesensing of parasitic nematodes in plants, now undergoing development, is based on measurement of visible and infrared spectral reflectances of fields where the plants are growing. Initial development efforts have been concentrated on detecting reniform nematodes (Rotylenchulus reniformis) in cotton plants, because of the economic importance of cotton crops. The apparatus includes a hand-held spectroradiometer. The readings taken by the radiometer are processed to extract spectral reflectances at sixteen wavelengths between 451 and 949 nm that, taken together, have been found to be indicative of the presence of Rotylenchulus reniformis. The intensities of the spectral reflectances are used to estimate the population density of the nematodes in an area from which readings were taken.

the uncertainty on the model results on the offshore wind resource, it is necessary to compare model results with observations. Observations from ground-based wind lidar and satellite remotesensing are the two main technologies that can provide new types of offshore wind data at relatively low cost....... The advantages of microwave satellite remotesensing are 1) horizontal spatial coverage, 2) long data archives and 3) high spatial detail both in the coastal zone and of far-field wind farm wake. Passive microwave ocean wind speed data are available since 1987 with up to 6 observations per day with near...

compared to other technologies: compared to field based techniques, remotesensing with UAVs is a non-destructive technique, less time consuming, ensures a reduced time between acquisition and interpretation of data and gives the possibility to access remote and unsafe areas. Compared to full...... will be able to record the spectral signatures of water and land surfaces with a pixel resolution of around 15 cm, whereas the thermal camera will sense water and land surface temperature with a resolution of 40 cm. Post-processing of data from the thermal camera will allow retrieving vegetation and soil...

Space RemoteSensing Systems: An Introduction discusses the space remotesensing system, which is a modern high-technology field developed from earth sciences, engineering, and space systems technology for environmental protection, resource monitoring, climate prediction, weather forecasting, ocean measurement, and many other applications. This book consists of 10 chapters. Chapter 1 describes the science of the atmosphere and the earth's surface. Chapter 2 discusses spaceborne radiation collector systems, while Chapter 3 focuses on space detector and CCD systems. The passive space optical rad

The author has identified the following significant results. A limited study was conducted to determine the applicability of remotesensing for evaluating water quality conditions in the San Francisco Bay and delta. Considerable supporting data were available for the study area from other than overflight sources, but short-term temporal and spatial variability precluded their use. The study results were not sufficient to shed much light on the subject, but it did appear that, with the present state of the art in image analysis and the large amount of ground truth needed, remotesensing has only limited application in monitoring water quality.

As humans, we perform remotesensing nearly all the time. This is because we acquire most of our information about our surroundings through the senses of sight and hearing. Whether viewed by the unenhanced eye or a military satellite, remotesensing is observing objects from a distance. With our current technology, remotesensing has become a part…

Full Text Available Stipa purpurea is the representative type of alpine grassland in Tibet and the surviving and development material for herdsmen. This paper takes Shenzha County as the research area. Based on the analysis of typical hyperspectral variables sensitive to chlorophyll content of Stipa purpurea, 10 spectral variables with significant correlation with chlorophyll were extracted. The estimation model of chlorophyll was established. The photosynthetic pigment contents in the Shenzha area were calculated by using HJ-1A remotesensing images. The results show that (1 there are significant correlations between chlorophyll content and spectral variables; in particular, the coefficient of Chlb in Stipa purpurea with RVI is the largest (0.728; (2 10 variables are correlated with chlorophyll, and the order of correlation is Chlb > Chla > Chls; (3 for the estimation of Chla, the EVI is the best variable. RVI, NDVI, and VI2 are suitable for Chlb; RVI and NDVI are also suitable for the estimation of Chls; (4 the mean estimated content of Chla in Stipa bungeana is about 4.88 times that of Chlb, while Cars is slightly more than Chlb; (5 the distribution of Chla is opposite to Chlb and Chls content in water area.

A fast infrared radiative transfer (RT) model is developed on the basis of the adding-doubling principle, hereafter referred to as FIRTM-AD, to facilitate the forward RT simulations involved in hyperspectral remote-sensing applications under cloudy-sky conditions. A pre-computed look-up table (LUT) of the bidirectional reflection and transmission functions and emissivities of ice clouds in conjunction with efficient interpolation schemes is used in FIRTM-AD to alleviate the computational burden of the doubling process. FIRTM-AD is applicable to a variety of cloud conditions, including vertically inhomogeneous or multilayered clouds. In particular, this RT model is suitable for the computation of high-spectral-resolution radiance and brightness temperature (BT) spectra at both the top-of-atmosphere and surface, and thus is useful for satellite and ground-based hyperspectral sensors. In terms of computer CPU time, FIRTM-AD is approximately 100-250 times faster than the well-known discrete-ordinate (DISORT) RT model for the same conditions. The errors of FIRTM-AD, specified as root-mean-square (RMS) BT differences with respect to their DISORT counterparts, are generally smaller than 0.1 K

In 2008, scientists from seven federal and state institutions worked together to investigate temporal and spatial variations of evapotranspiration (ET) and surface energy balance in a semi-arid irrigated and dryland agricultural region of the Southern High Plains in the Texas Panhandle. This Bushland Evapotranspiration and Agricultural Remotesensing EXperiment 2008 (BEAREX08) involved determination of micrometeorological fluxes (surface energy balance) in four weighing lysimeter fields (each 4.7 ha) containing irrigated and dryland cotton and in nearby bare soil, wheat stubble and rangeland fields using nine eddy covariance stations, three large aperture scintillometers, and three Bowen ratio systems. In coordination with satellite overpasses, flux and remotesensing aircraft flew transects over the surrounding fields and region encompassing an area contributing fluxes from 10 to 30 km upwind of the USDA-ARS lysimeter site. Tethered balloon soundings were conducted over the irrigated fields to investigate the effect of advection on local boundary layer development. Local ET was measured using four large weighing lysimeters, while field scale estimates were made by soil water balance with a network of neutron probe profile water sites and from the stationary flux systems. Aircraft and satellite imagery were obtained at different spatial and temporal resolutions. Plot-scale experiments dealt with row orientation and crop height effects on spatial and temporal patterns of soil surface temperature, soil water content, soil heat flux, evaporation from soil in the interrow, plant transpiration and canopy and soil radiation fluxes. The BEAREX08 field experiment was unique in its assessment of ET fluxes over a broad range in spatial scales; comparing direct and indirect methods at local scales with remotesensing based methods and models using aircraft and satellite imagery at local to regional scales, and comparing mass balance-based ET ground truth with eddy covariance

This paper presents a combination of techniques suitable for remotelysensing foliar Nitrogen (N) in semiarid shrublands – a capability that would significantly improve our limited understanding of vegetation functionality in dryland ecosystems. The ability to estimate foliar N distributions across arid and semi-arid environments could help answer process-driven questions related to topics such as controls on canopy photosynthesis, the influence of N on carbon cycling behavior, nutrient pulse dynamics, and post-fire recovery. Our study determined that further exploration into estimating sagebrush canopy N concentrations from an airborne platform is warranted, despite remotesensing challenges inherent to open canopy systems. Hyperspectral data transformed using standard derivative analysis were capable of quantifying sagebrush canopy N concentrations using partial least squares (PLS) regression with an R2 value of 0.72 and an R2 predicted value of 0.42 (n = 35). Subsetting the dataset to minimize the influence of bare ground (n = 19) increased R2 to 0.95 (R2 predicted = 0.56). Ground-based estimates of canopy N using leaf mass per unit area measurements (LMA) yielded consistently better model fits than ground-based estimates of canopy N using cover and height measurements. The LMA approach is likely a method that could be extended to other semiarid shrublands. Overall, the results of this study are encouraging for future landscape scale N estimates and represent an important step in addressing the confounding influence of bare ground, which we found to be a major influence on predictions of sagebrush canopy N from an airborne platform.

Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remotesensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remotesensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remotesensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remotesensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remotesensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application. PMID:25215941

Full Text Available Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remotesensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remotesensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remotesensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remotesensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remotesensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.

Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remotesensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remotesensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remotesensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remotesensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remotesensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.

Image edge-extraction is an important step in image processing and recognition, and also a hot spot in science study. In this paper, based on primary methods of the remotesensing image edge-extraction, authors, for the first time, have proposed several elements which should be considered before processing. Then, the qualities of several methods in remotesensing image edge-extraction are systematically summarized. At last, taking Near Nasca area (Peru) as an example the edge-extraction of Magmatic Range is analysed. (authors)

Atmospheric particulate pollutants not only reduce atmospheric visibility, change the energy balance of the troposphere, but also affect human and vegetation health. For monitoring the particulate pollutants, we establish and develop a series of inversion algorithms based on polarimetric remotesensing technology which has unique advantages in dealing with atmospheric particulates. A solution is pointed out to estimate the near surface PM2.5 mass concentrations from full remotesensing measurements including polarimetric, active and infrared remotesensing technologies. It is found that the mean relative error of PM2.5 retrieved by full remotesensing measurements is 35.5 % in the case of October 5th 2013, improved to a certain degree compared to previous studies. A systematic comparison with the ground-based observations further indicates the effectiveness of the inversion algorithm and reliability of results. A new generation of polarized sensors (DPC and PCF), whose observation can support these algorithms, will be onboard GF series satellites and launched by China in the near future.

Satellite remotesensing of ocean surface winds are presented with focus on wind energy applications. The history on operational and research-based satellite ocean wind mapping is briefly described for passive microwave, scatterometer and synthetic aperture radar (SAR). Currently 6 GW installed...

The purpose of this publication is to provide the reader with a basis for making an intelligent approach to the use of remotesensing in uranium exploration. It includes: A description of the various techniques; specific applications in view of exploration strategy and selection of appropriate techniques, and some examples of applications; availability and costs; a bibliography

Remotesensing technology has the potential to enhance the engagement of communities and managers in the implementation and performance of best management practices. This presentation will use examples from U.S. numeric criteria development and state water quality monitoring prog...

Full Text Available Atmospheric particulate pollutants not only reduce atmospheric visibility, change the energy balance of the troposphere, but also affect human and vegetation health. For monitoring the particulate pollutants, we establish and develop a series of inversion algorithms based on polarimetric remotesensing technology which has unique advantages in dealing with atmospheric particulates. A solution is pointed out to estimate the near surface PM2.5 mass concentrations from full remotesensing measurements including polarimetric, active and infrared remotesensing technologies. It is found that the mean relative error of PM2.5 retrieved by full remotesensing measurements is 35.5 % in the case of October 5th 2013, improved to a certain degree compared to previous studies. A systematic comparison with the ground-based observations further indicates the effectiveness of the inversion algorithm and reliability of results. A new generation of polarized sensors (DPC and PCF, whose observation can support these algorithms, will be onboard GF series satellites and launched by China in the near future.

In order to quantify the rates of the exchanges of energy and matter among hydrosphere, biosphere and atmosphere, quantitative description of land surface processes by means of measurements at different scales are essential. Quantitative remotesensing plays an important role in this respect. The

Video imagery can be acquired from aerial, terrestrial and marine based platforms and has been exploited for a range of remotesensing applications over the past two decades. Examples include coastal surveys using aerial video, routecorridor infrastructures surveys using vehicle mounted video cameras, aerial surveys over forestry and agriculture, underwater habitat mapping and disaster management. Many of these video systems are based on interlaced, television standards such as North America's NTSC and European SECAM and PAL television systems that are then recorded using various video formats. This technology has recently being employed as a front-line, remotesensing technology for damage assessment post-disaster. This paper traces the development of spatial video as a remotesensing tool from the early 1980s to the present day. The background to a new spatial-video research initiative based at National University of Ireland, Maynooth, (NUIM) is described. New improvements are proposed and include; low-cost encoders, easy to use software decoders, timing issues and interoperability. These developments will enable specialists and non-specialists collect, process and integrate these datasets within minimal support. This integrated approach will enable decision makers to access relevant remotelysensed datasets quickly and so, carry out rapid damage assessment during and post-disaster.

The purpose of this publication is to provide the reader with a basis for making an intelligent approach to the use of remotesensing in uranium exploration. It includes: A description of the various techniques; specific applications in view of exploration strategy and selection of appropriate techniques, and some examples of applications; availability and costs; a bibliography.

Semiconductor injection lasers are required for implementing virtually all spaceborne remotesensing systems. Their main advantages are high reliability and efficiency, and their main roles are envisioned in pumping and injection locking of solid state lasers. In some shorter range applications they may even be utilized directly as the sources.

Remotesensing techniques hold considerable promise for the inventory and monitoring of natural resources on rangelands. A significant lack of information concerning basic spectral characteristics of range vegetation and soils has resulted in a lack of rangeland applications. The parameters of interest for range condition ...

In this paper we present the usage of photonic remote laser based device for sensing nano-vibrations for detection of muscle contraction and fatigue, eye movements and in-vivo estimation of glucose concentration. The same concept is also used to realize a remote optical stethoscope. The advantage of doing the measurements from a distance is in preventing passage of infections as in the case of optical stethoscope or in the capability to monitor e.g. sleep quality without disturbing the patient. The remote monitoring of glucose concentration in the blood stream and the capability to perform opto-myography for the Messer muscles (chewing) is very useful for nutrition and weight control. The optical configuration for sensing the nano-vibrations is based upon analyzing the statistics of the secondary speckle patterns reflected from various tissues along the body of the subjects. Experimental results present the preliminary capability of the proposed configuration for the above mentioned applications.

This is the final report of a one-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The science and technology of satellite remotesensing is an emerging interdisciplinary field that is growing rapidly with many global and regional applications requiring quantitative sensing of earth`s surface features as well as its atmosphere from space. It is possible today to resolve structures on the earth`s surface as small as one meter from space. If this high spatial resolution is coupled with high spectral resolution, instant object identification can also be achieved. To interpret these spectral signatures correctly, it is necessary to perform a computational correction on the satellite imagery that removes the distorting effects of the atmosphere. This project studied such new concepts and applied innovative new approaches in remotesensing science.

Several international conventions and agreements have stressed the importance of the assessment of forest biodiversity. However, the methods by which such assessments can be made remain unclear. Remotesensing represents an important tool for looking at ecosystem diversity and various structural aspects of individual ecosystems. It provides a means to make assessments across several different spatial scales, and is also critical for assessments of changes in ecosystem pattern over time. Many different forms of remotesensing are available. While lately the emphasis on laser scanner and synthetic aperture radar data has increased, most work to date has used photographs and digital optical imagery, primarily from airborne and spaceborne platforms. These provide the opportunity to assess different phenomena from the landscape to the stand scale. Remotesensing provides the most efficient tool available for determining landscape-scale elements of forest biodiversity, such as the relative proportion of matrix and patches and their physical arrangement. At intermediate scales, remotesensing provides an ideal tool for evaluating the presence of corridors and the nature of edges. At the stand scale, remotesensing technologies are likely to deliver an increasing amount of information about the structural attributes of forest stands, such as the nature of the canopy surface, the presence of layering within the canopy and presence of (very) coarse woody debris on the forest floor. Given the rate of development in the technology, even greater usage is likely in the future. (author)

The aerodynamic roughness is one of the major parameters in describing the turbulent exchange process between terrestrial and atmosphere. RemoteSensing is recognized as an effective way to inverse this parameter at the regional scale. However, in the long time the inversion method is either dependent on the lookup table for different land covers or the Normalized Difference Vegetation Index (NDVI) factor only, which plays a very limited role in describing the spatial heterogeneity of this parameter and the evapotranspiration (ET) for different land covers. In fact, the aerodynamic roughness is influenced by different factors at the same time, including the roughness unit for hard surfaces, the vegetation dynamic growth and the undulating terrain. Therefore, this paper aims at developing an innovative aerodynamic roughness inversion method based on multi-source remotesensing data in a semiarid region, within the upper and middle reaches of Heihe River Basin. The radar backscattering coefficient was used to inverse the micro-relief of the hard surface. The NDVI was utilized to reflect the dynamic change of vegetated surface. Finally, the slope extracted from SRTM DEM (Shuttle Radar Topography Mission Digital Elevation Model) was used to correct terrain influence. The inversed aerodynamic roughness was imported into ETWatch system to validate the availability. The inversed and tested results show it plays a significant role in improving the spatial heterogeneity of the aerodynamic roughness and related ET for the experimental site

With the purpose of providing scientific basis for environmental planning about non-point source pollution prevention and control, and improving the pollution regulating efficiency, this paper established the Grid Landscape Contrast Index based on Location-weighted Landscape Contrast Index according to the “source–sink” theory. The spatial distribution of non-point source pollution caused by Jiulongjiang Estuary could be worked out by utilizing high resolution remotesensing images. The results showed that, the area of “source” of nitrogen and phosphorus in Jiulongjiang Estuary was 534.42 km 2 in 2008, and the “sink” was 172.06 km 2 . The “source” of non-point source pollution was distributed mainly over Xiamen island, most of Haicang, east of Jiaomei and river bank of Gangwei and Shima; and the “sink” was distributed over southwest of Xiamen island and west of Shima. Generally speaking, the intensity of “source” gets weaker along with the distance from the seas boundary increase, while “sink” gets stronger. -- Highlights: •We built an index to study the “source–sink” structure of NSP in a space scale. •The Index was applied in Jiulongjiang estuary and got a well result. •The study is beneficial to discern the high load area of non-point source pollution. -- “Source–Sink” Structure of non-point source nitrogen and phosphorus pollution in Jiulongjiang estuary in China was worked out by the Grid Landscape Contrast Index

This report describes work carried out under the Air Force Research Laboratory's basic research task in optical remote-sensing signatures, entitled Optical / Infrared Signatures for Space-Based RemoteSensing...

Opportunities for Increasing Societal Value of RemoteSensing Data in South Africa's Strategic Development Priorities: A Review. ... Despite the enormous capital required to fund remotesensing initiatives, governments ... HOW TO USE AJOL.

Assessing the accuracy of remotesensing techniques in vegetation fractions estimation. ... This study aimed at exploring different remotesensing (RS) techniques for quantitatively measuring vegetation and bare soil ... HOW TO USE AJOL.

-Natal and MONDI Business Paper have recently embarked on a remotesensing cooperative. The primary focus of this cooperative is to explore the potential benefits associated with using remotesensing for forestry-related activities.

Remotesensing technology plays an important role in monitoring rapid changes of the Earth's surface. However, sensors that can simultaneously provide satellite images with both high temporal and spatial resolution haven't been designed yet. This paper proposes an improved spatial and temporal adaptive reflectance fusion model (STARFM) with the help of an Unmixing-based method (USTARFM) to generate the high spatial and temporal data needed for the study of heterogeneous areas. The results showed that the USTARFM had higher accuracy than STARFM methods in two aspects of analysis: individual bands and of heterogeneity analysis. Taking the predicted NIR band as an example, the correlation coefficients (r) for the USTARFM, STARFM and unmixing methods were 0.96, 0.95, 0.90, respectively (p-value data fusion problems faced when using STARFM. Additionally, the USTARFM method could help researchers achieve better performance than STARFM at a smaller window size from its heterogeneous land surface quantitative representation.

A technique for sensing a moving object within a physical environment using a MIMO communication link includes generating a channel matrix based upon channel state information of the MIMO communication link. The physical environment operates as a communication medium through which communication signals of the MIMO communication link propagate between a transmitter and a receiver. A spatial information variable is generated for the MIMO communication link based on the channel matrix. The spatial information variable includes spatial information about the moving object within the physical environment. A signature for the moving object is generated based on values of the spatial information variable accumulated over time. The moving object is identified based upon the signature.

Unmanned air vehicle remotely-sensed imagery on the low-altitude has the advantages of higher revolution, easy-shooting, real-time accessing, etc. It's been widely used in mapping , target identification, and other fields in recent years. However, because of conditional limitation, the video images are unstable, the targets move fast, and the shooting background is complex, etc., thus it is difficult to process the video images in this situation. In other fields, especially in the field of computer vision, the researches on video images are more extensive., which is very helpful for processing the remotely-sensed imagery on the low-altitude. Based on this, this paper analyzes and summarizes amounts of video image processing achievement in different fields, including research purposes, data sources, and the pros and cons of technology. Meantime, this paper explores the technology methods more suitable for low-altitude video image processing of remotesensing.

Remotesensing has been applied in the past to the surveillance of Great Lakes water quality, but it has been only partially successful because of the completely empirical approach taken in relating the multispectral scanning data at visible and near-infrared wavelengths to water parameters. Any remotesensing approach using water color information must take into account (1) the existence of many different organic and inorganic species throughtout the Greak Lakes, (2) the occurrence of a mixture of species in most locations, and (3) spatial (inter- and interlake as well as vertical) variations in types and concentrations of species. The radiative transfer model provides a potential method for an orderly analysis of remotesensing data and a physical basis for developing quantitative algorithms. Predictions and field measurements of volume reflectances are presented which clearly show the advantage of using a radiative transfer model. Spectral absorptance and backscattering coefficients for two inorganic sediments are reported

Full Text Available This paper introduces the processing technology of high resolution remotesensing image, the specific making process of tourism map and different remotesensing data in the key application of tourism planning and so on. Remotesensing extracts agricultural tourism planning information, improving the scientificalness and operability of agricultural tourism planning. Therefore remotesensing image in the application of agricultural tourism planning will be the inevitable trend of tourism development.

Full Text Available coastal resources and anthropogenic infrastructure for a safer future. What is the role of remotesensing? The coastal zone connects terrestrial biophysical systems with marine systems. Some marine ecosystems cannot function without intact inland... for the development of sound integrated management solutions. To date, however, remotesensing applications usually focus on areas landward from the highwater line (?terrestrial? remotesensing), while ?marine? remotesensing does not pay attention to the shallow...

The use of airborne remotesensing techniques to: (1) detect drainage problem areas, (2) delineate the problem in terms of areal extent, depth to the water table, and presence of excessive salinity, and (3) evaluate the effectiveness of existing subsurface drainage facilities, is discussed. Experimental results show that remotesensing, as demonstrated in this study and as presently constituted and priced, does not represent a practical alternative as a management tool to presently used visual and conventional photographic methods in the systematic and repetitive detection and delineation of wetlands.

Noise estimation does not receive much attention in remotesensing society. It may be because normally noise is not large enough to impair image analysis result. Noise estimation is also very challenging due to the randomness nature of the noise (for random noise) and the difficulty of separating the noise component from the signal in each specific location. We review and propose seven different types of methods to estimate noise variance and noise covariance matrix in a remotelysensed image. In the experiment, it is demonstrated that a good noise estimate can improve the performance of an algorithm via noise whitening if this algorithm assumes white noise.

A historical overview of the discovery and development of photography, related sciences, and remotesensing technology is presented. The role of education to date in the development of remotesensing is discussed. The probable future and potential of remotesensing and training is described.

Recent studies have highlighted the potential role of water in the transmission of avian influenza (AI) viruses and the existence of often interacting variables that determine the survival rate of these viruses in water; the two main variables are temperature and salinity. Remotesensing has been used to map and monitor water bodies for several decades. In this paper, we review satellite image analysis methods used for water detection and characterization, focusing on the main variables that influence AI virus survival in water. Optical and radar imagery are useful for detecting water bodies at different spatial and temporal scales. Methods to monitor the temperature of large water surfaces are also available. Current methods for estimating other relevant water variables such as salinity, pH, turbidity and water depth are not presently considered to be effective.

A method and apparatus for remote, stand-off, and high efficiency spectroscopic detection of biological and chemical substances. The apparatus including an optical beam transmitter which transmits a beam having an axis of transmission to a target, the beam comprising at least a laser emission. An optical detector having an optical detection path to the target is provided for gathering optical information. The optical detection path has an axis of optical detection. A beam alignment device fixes the transmitter proximal to the detector and directs the beam to the target along the optical detection path such that the axis of transmission is within the optical detection path. Optical information gathered by the optical detector is analyzed by an analyzer which is operatively connected to the detector.

Exploiting geothermal (GT) resources requires first and foremost locating suitable areas for its development. Remotesensing offers a synoptic capability of covering large areas in real time and can cost effectively explore prospective geothermal sites not easily detectable using conventional survey methods, thus can aid in ...

Full Text Available An autonomous surface vehicle instrumented with optical and acoustical sensors was deployed in Kane'ohe Bay, HI, U.S.A., to provide high-resolution, in situ observations of coral reef reflectance with minimal human presence. The data represented a wide range in bottom type, water depth, and illumination and supported more thorough investigations of remotesensingmethods for identifying and mapping shallow reef features. The in situ data were used to compute spectral bottom reflectance and remotesensing reflectance, Rrs,λ, as a function of water depth and benthic features. The signals were used to distinguish between live coral and uncolonized sediment within the depth range of the measurements (2.5–5 m. In situRrs, λ were found to compare well with remotelysensed measurements from an imaging spectrometer, the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS, deployed on an aircraft at high altitude. Cloud cover and in situ sensor orientation were found to have minimal impact on in situRrs, λ, suggesting that valid reflectance data may be collected using autonomous surveys even when atmospheric conditions are not favorable for remotesensing operations. The use of reflectance in the red and near infrared portions of the spectrum, expressed as the red edge height, REHλ, was investigated for detecting live aquatic vegetative biomass, including coral symbionts and turf algae. The REHλ signal from live coral was detected in Kane'ohe Bay to a depth of approximately 4 m with in situ measurements. A remotesensing algorithm based on the REHλ signal was defined and applied to AVIRIS imagery of the entire bay and was found to reveal areas of shallow, dense coral and algal cover. The peak wavelength of REHλ decreased with increasing water depth, indicating that a more complete examination of the red edge signal may potentially yield a remotesensing approach to simultaneously estimate vegetative biomass and bathymetry in shallow water.

RemoteSensing Application (RSA) is important as one of the critical enabler of e-systems such as e- governments, e-commerce, and e-sciences. In this study, we argued that owning to the specialized needs of RSA such as volatility and interactive nature, a customized Software Engineering (SE) approach should be adapted for their development. Based on this argument we have also identified the shortcomings of the conventional SE approaches and the classical waterfall software development life cycle model. In this study, we have proposed a modification to the classical waterfall software development life cycle model for proposing a customized software development Framework for RSAs. We have identified four (4) different types of changes that can occur to an already developed RS application. The proposed framework was capable to incorporate all four types of changes. RemoteSensing, software engineering, functional requirements, types of changes. (author)

The National Satellite Land RemoteSensing Data Archive is managed on behalf of the Secretary of the Interior by the U.S. Geological Survey’s Earth Resources Observation and Science Center. The Land RemoteSensing Policy Act of 1992 (51 U.S.C. §601) directed the U.S. Department of the Interior to establish a permanent global archive consisting of imagery over land areas obtained from satellites orbiting the Earth. The law also directed the U.S. Department of the Interior, delegated to the U.S. Geological Survey, to ensure proper storage and preservation of imagery, and timely access for all parties. Since 2008, these images have been available at no cost to the user.

In June and July of 1997, the US Department of Energy, in cooperation with the Republic of Kazakhstan Ministry of Science - Academy of Science conducted a remotesensing mission to Kazakhstan. The mission was conducted as a technology demonstration under a Memorandum of Understanding between the United States Department of Energy and the Republic of Kazakhstan's Ministry of science - Academy of Science. The mission was performed using a US Navy P-3 Orion aircraft and imaging capabilities developed by the Department of Energy's Office of Non-proliferation and National Security. The imaging capabilities consisted of two imaging pods - a synthetic aperture radar (SAR) pod and a multi sensor imaging pod (MSI). Seven experiments were conducted to demonstrate how remotesensing can be used to support city planning, land cover mapping, mineral exploration, and non-proliferation monitoring. Results of the mission will be presented

Although the Federation does not sponsor or undertake surveillance and remotesensing research and development projects, it is a potential user of remotesensing equipment when responding to oil spills. Indeed, the Federation has already made use of suitably equipped aircraft on a number of occasions in Europe. Several countries in north west Europe, viz. France, Germany, Netherlands, Norway, Sweden and the U.K., operate aircraft fitted with broadly similar systems comprising side-looking airborne radar (SLAR), infra-red line scanners (IRLS) and ultra-violet line scanners (UVLS). These aircraft are used routinely for the detection of operational discharges of oil from ships in violation of the International Convention on the Prevention of Pollution from Ships 73/78 (MARPOL 73/78)

Traditional commercial remotesensing has focused on the geologic market, with primary focus on mineral identification and mapping in the visible through short-wave infrared spectral regions (0.4 to 2.4 microns). Commercial remotesensing users now demand airborne scanning capabilities spanning the entire wavelength range from ultraviolet through thermal infrared (0.3 to 12 microns). This spectral range enables detection, identification, and mapping of objects and liquids on the earth's surface and gases in the air. Applications requiring this range of wavelengths include detection and mapping of oil spills, soil and water contamination, stressed vegetation, and renewable and non-renewable natural resources, and also change detection, natural hazard mitigation, emergency response, agricultural management, and urban planning. GER has designed and built a configurable scanner that acquires high resolution images in 63 selected wave bands in this broad wavelength range.

Aplicaciones Cientificas-C (SAC-C) satellites. CHAMP provided 8 years of radio oc- cultation data consisting of around 440,000 measurements from February...applications, various modifi- cations of terrestrial receivers are required, including hardware and software modifications to enhance surviv- ability in a...Dop- pler shifts. On the other hand, special hardware and software is required to support non-navigation remotesensing applications in space, such

The volume of remotelysensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. This dissertation proposes and implements an extensive remotesensing image information mining (ReSIM) system prototype for mining useful information implicitly stored in remotesensing imagery. The system consists of three modules: image processing subsystem, database subsystem, and visualization and graphical user interface (GUI) subsystem. Land cover and land use (LCLU) information corresponding to spectral characteristics is identified by supervised classification based on support vector machines (SVM) with automatic model selection, while textural features that characterize spatial information are extracted using Gabor wavelet coefficients. Within LCLU categories, textural features are clustered using an optimized k-means clustering approach to acquire search efficient space. The clusters are stored in an object-oriented database (OODB) with associated images indexed in an image database (IDB). A k-nearest neighbor search is performed using a query-by-example (QBE) approach. Furthermore, an automatic parametric contour tracing algorithm and an O(n) time piecewise linear polygonal approximation (PLPA) algorithm are developed for shape information mining of interesting objects within the image. A fuzzy object-oriented database based on the fuzzy object-oriented data (FOOD) model is developed to handle the fuzziness and uncertainty. Three specific applications are presented: integrated land cover and texture pattern mining, shape information mining for change detection of lakes, and

Today, there are more than eight thousand satellites in space. Therefore, Radio Frequency (RF) signals broadcast from satellites can be accessed from almost every point on the earth. There will be number of satellites available at most points on earth with different frequency bands. These satellite signals can be used for remotesensing, therefore software that visualizes footprints of satellites and shows characteristics of every satellite available at any point would be useful in determinin...

remotesensing , cyclonic scale diagnostic studies and mesoscale numerical modeling and forecasting are summarized. Mechanisms involved in the release of potential instability are discussed and simulated quantitatively, giving particular attention to the convective formulation. The basic mesoscale model is documented including the equations, boundary condition, finite differences and initialization through an idealized frontal zone. Results of tests including a three dimensional test with real data, tests of convective/mesoscale interaction and tests with a detailed

Video imagery can be acquired from aerial, terrestrial and marine based platforms and has been exploited for a range of remotesensing applications over the past two decades. Examples include coastal surveys using aerial video, routecorridor infrastructures surveys using vehicle mounted video cameras, aerial surveys over forestry and agriculture, underwater habitat mapping and disaster management. Many of these video systems are based on interlaced, television standards such as North...

To solve the problem that the remotesensing image segmentation speed is slow and the real-time performance is poor, this paper studies the method of remotesensing image segmentation based on Hadoop platform. On the basis of analyzing the structural characteristics of Hadoop cloud platform and its component MapReduce programming, this paper proposes a method of image segmentation based on the combination of OpenCV and Hadoop cloud platform. Firstly, the MapReduce image processing model of Hadoop cloud platform is designed, the input and output of image are customized and the segmentation method of the data file is rewritten. Then the Mean Shift image segmentation algorithm is implemented. Finally, this paper makes a segmentation experiment on remotesensing image, and uses MATLAB to realize the Mean Shift image segmentation algorithm to compare the same image segmentation experiment. The experimental results show that under the premise of ensuring good effect, the segmentation rate of remotesensing image segmentation based on Hadoop cloud Platform has been greatly improved compared with the single MATLAB image segmentation, and there is a great improvement in the effectiveness of image segmentation.

Full Text Available A combination of geophysical methods could be very a useful and a practical way of verifying the origin and precise localisation of archaeological situations identified by different remotesensing techniques. The results of different methods (and scales of monitoring these fully non-destructive methods provide distinct data and often complement each other. The presented examples of combinations of these methods/techniques in this study (aerial survey, LIDAR-ALS and surface magnetometer or resistivity survey could provide information on some specifics and may also be limitations in surveying different archaeological terrains, types of archaeological situations and activities. The archaeological site in this contribution is considered to be a material of this study. In case of Neolithic ditch enclosure near Kolín were compared aerial prospection data, magnetometer survey and aerial photo-documentation of excavated site. In the case of hillforts near Levousy we compared LIDAR data with aerial photography and large-scale magnetometer survey. In the case of the medieval castle Liběhrad we compared LIDAR data with geoelectric resistivity measurement. In case of a burial mound cemetery we combined LIDAR data with magnetometer survey. In the case of the production area near Rynartice we combined LIDAR data with magnetometer and resistivity measurements and result of archaeological excavation. Fortunately for successful combination of geophysical and remotesensing results, their conditions and factors for efficient use in archaeology are not the same. On the other hand, the quality and state of many prehistoric, early medieval, medieval and also modern archaeological sites is rapidly changing over time and both groups of techniques represent important support for their comprehensive and precise documentation and protection.

This paper focuses on the use of remotesensing for marine oil spill detection and response. The surveillance and monitoring of discharges, and the main elements of effective surveillance are discussed. Tactical emergency response and the requirements for selecting a suitable remotesensing approach, airborne remotesensing systems, and the integration of satellite and airborne imaging are examined. Specifications of satellite surveillance systems potentially usable for oil spill detection, and specifications of airborne remotesensing systems suitable for oil spill detection, monitoring and supplemental actions are tabulated, and a schema of integrated satellite-airborne remotesensing (ISARS) is presented. (UK)

RemoteSensing of Landscapes with Spectral Images describes how to process and interpret spectral images using physical models to bridge the gap between the engineering and theoretical sides of remote-sensing and the world that we encounter when we venture outdoors. The emphasis is on the practical use of images rather than on theory and mathematical derivations. Examples are drawn from a variety of landscapes and interpretations are tested against the reality seen on the ground. The reader is led through analysis of real images (using figures and explanations); the examples are chosen to illustrate important aspects of the analytic framework. This textbook will form a valuable reference for graduate students and professionals in a variety of disciplines including ecology, forestry, geology, geography, urban planning, archeology and civil engineering. It is supplemented by a web-site hosting digital color versions of figures in the book as well as ancillary images (www.cambridge.org/9780521662214). Presents a coherent view of practical remotesensing, leading from imaging and field work to the generation of useful thematic maps Explains how to apply physical models to help interpret spectral images Supplemented by a website hosting digital colour versions of figures in the book, as well as additional colour figures

This slide presentation reviews some of the issues in quality of remotesensing data. Data "quality" is used in several different contexts in remotesensing data, with quite different meanings. At the pixel level, quality typically refers to a quality control process exercised by the processing algorithm, not an explicit declaration of accuracy or precision. File level quality is usually a statistical summary of the pixel-level quality but is of doubtful use for scenes covering large areal extents. Quality at the dataset or product level, on the other hand, usually refers to how accurately the dataset is believed to represent the physical quantities it purports to measure. This assessment often bears but an indirect relationship at best to pixel level quality. In addition to ambiguity at different levels of granularity, ambiguity is endemic within levels. Pixel-level quality terms vary widely, as do recommendations for use of these flags. At the dataset/product level, quality for low-resolution gridded products is often extrapolated from validation campaigns using high spatial resolution swath data, a suspect practice at best. Making use of quality at all levels is complicated by the dependence on application needs. We will present examples of the various meanings of quality in remotesensing data and possible ways forward toward a more unified and usable quality framework.

This report concerns the feasibility of using remotely-sensed data for long-term monitoring of uranium tailings. Decommissioning of uranium mine tailings sites may require long-term monitoring to confirm that no unanticipated release of contaminants occurs. Traditional ground-based monitoring of specific criteria of concern would be a significant expense depending on the nature and frequency of the monitoring. The objective of this study was to evaluate whether available remote-sensing data and techniques were applicable to the long-term monitoring of tailings sites. This objective was met by evaluating to what extent the data and techniques could be used to identify and discriminate information useful for monitoring tailings sites. The cost associated with obtaining and interpreting this information was also evaluated. Satellite and aircraft remote-sensing-based activities were evaluated. A monitoring programme based on annual coverage of Landsat Thematic Mapper data is recommended. Immediately prior to and for several years after decommissioning of the tailings sites, airborne multispectral and thermal infrared surveys combined with field verification data are required in order to establish a baseline for the long-term satellite-based monitoring programme. More frequent airborne surveys may be required if rapidly changing phenomena require monitoring. The use of a geographic information system is recommended for the effective storage and manipulation of data accumulated over a number of years

Full Text Available The present paper aims at analyzing the potentialities of noninvasive remotesensing techniques used for detecting the conservation status of infrastructures. The applied remotesensing techniques are ground-based microwave radar interferometer and InfraRed Thermography (IRT to study a particular structure planned and made in the framework of the ISTIMES project (funded by the European Commission in the frame of a joint Call “ICT and Security” of the Seventh Framework Programme. To exploit the effectiveness of the high-resolution remotesensing techniques applied we will use the high-frequency thermal camera to measure the structures oscillations by high-frequency analysis and ground-based microwave radar interferometer to measure the dynamic displacement of several points belonging to a large structure. The paper describes the preliminary research results and discusses on the future applicability and techniques developments for integrating high-frequency time series data of the thermal imagery and ground-based microwave radar interferometer data.

Full Text Available A wide range of satellite methods is applied now in seismology. The first applications of satellite data for earthquake exploration were initiated in the ‘70s, when active faults were mapped on satellite images. It was a pure and simple extrapolation of airphoto geological interpretation methods into space. The modern embodiment of this method is alignment analysis. Time series of alignments on the Earth's surface are investigated before and after the earthquake. A further application of satellite data in seismology is related with geophysical methods. Electromagnetic methods have about the same long history of application for seismology. Stable statistical estimations of ionosphere-lithosphere relation were obtained based on satellite ionozonds. The most successful current project "DEMETER" shows impressive results. Satellite thermal infra-red data were applied for earthquake research in the next step. Numerous results have confirmed previous observations of thermal anomalies on the Earth's surface prior to earthquakes. A modern trend is the application of the outgoing long-wave radiation for earthquake research. In ‘80s a new technology—satellite radar interferometry—opened a new page. Spectacular pictures of co-seismic deformations were presented. Current researches are moving in the direction of pre-earthquake deformation detection. GPS technology is also widely used in seismology both for ionosphere sounding and for ground movement detection. Satellite gravimetry has demonstrated its first very impressive results on the example of the catastrophic Indonesian earthquake in 2004. Relatively new applications of remotesensing for seismology as atmospheric sounding, gas observations, and cloud analysis are considered as possible candidates for applications.

Full Text Available Over the last several decades, remotesensing has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. The objective of this review, which summarizes the methods and the results of existing remotesensing studies, is to synthesize principle findings and assess the state of the art. We take a taxonomic approach to group studies based on location, scale, inputs, and methods, in an effort to categorize different approaches within a logical framework. We seek to evaluate the ability of remotesensing to provide synoptic and timely coverage of irrigated lands in several spectral regions. We also investigate the value of archived data that enable comparison of images through time. This overview of the studies to date indicates that remotesensing-based monitoring of irrigation is at an intermediate stage of development at local scales. For instance, there is overwhelming consensus on the efficacy of vegetation indices in identifying irrigated fields. Also, single date imagery, acquired at peak growing season, may suffice to identify irrigated lands, although to multi-date image data are necessary for improved classification and to distinguish different crop types. At local scales, the mapping of irrigated lands with remotesensing is also strongly affected by the timing of image acquisition and the number of images used. At the regional and global scales, on the other hand, remotesensing has not been fully operational, as methods that work in one place and time are not necessarily transferable to other locations and periods. Thus, at larger scales, more work is required to indentify the best spectral indices, best time periods, and best classification methods under different climatological and cultural environments. Existing studies at regional scales also establish the fact that both remotesensing and national statistical approaches require further refinement with a substantial investment of

New types of multispectral data and computer enhancement of images provide a basis for quantitative analysis of ground reflectance, colour discrimination and removal of illumination-geometry effects, not possible with standard aerial photographs. These methods can be designed to take advantage of spectral characteristics of minerals such as hematite and limonite in attempting to discriminate areas of alteration around mineral deposits. The spectral bands of Landsat are not optimum for this discrimination, but several studies show that enhancement of Landsat images permits effective mapping of altered ground in some areas. Red and yellow ground may be confused, a problem where only one of these colours marks alteration related to mineralization. Altered ground in uranium areas has been successfully defined at Cameron, Arizona and Crooks Gap and the Powder River Basin in Wyoming. The Wyoming studies, described in some detail, resulted in unambiguous discrimination of red alteration at Crooks Gap but only partial distinction of red altered ground from yellow-weathering areas in the Powder River Basin. In South Texas, heavy vegetation severely limits the detection of reflectance differences in geological materials or of structural features. Thermal-infrared images of the Texas area aid in detection and mapping of channel-fill deposits, potential loci of uranium mineralization in the Miocene Catahoula Tuff. (author)

We discuss the evolution and state-of-the-art of the use of Unmanned Aerial Systems (UAS) in the field of Photogrammetry and RemoteSensing (PaRS). UAS, Remotely-Piloted Aerial Systems, Unmanned Aerial Vehicles or simply, drones are a hot topic comprising a diverse array of aspects including technology, privacy rights, safety and regulations, and even war and peace. Modern photogrammetry and remotesensing identified the potential of UAS-sourced imagery more than thirty years ago. In the last five years, these two sister disciplines have developed technology and methods that challenge the current aeronautical regulatory framework and their own traditional acquisition and processing methods. Navety and ingenuity have combined off-the-shelf, low-cost equipment with sophisticated computer vision, robotics and geomatic engineering. The results are cm-level resolution and accuracy products that can be generated even with cameras costing a few-hundred euros. In this review article, following a brief historic background and regulatory status analysis, we review the recent unmanned aircraft, sensing, navigation, orientation and general data processing developments for UAS photogrammetry and remotesensing with emphasis on the nano-micro-mini UAS segment.

Close-range measurement combined with modelling of incoming radiation is used to evaluate the prospect of remotely-measuring net radiation of a wetland environment located in the Sand Hills of Nebraska. Results indicate that net radiation can be measured with an accuracy comparable to that of conventional instruments. Sources of error are identified and discussed. Possible application of the methodology to satellite remotesensing is considered. (author)

Monitoring of the earth's surface by remotesensing in the short-wave band can provide quick identification of some characteristics of natural systems. This band range allows one to diagnose subsurface aspects of the earth, as the scattering parameter is affected by irregularities in the dielectric permittivity of subsurface structures. This method based on the organization of the monitoring probe may detect changes in these environments, for example, to assess seismic hazard, hazardous natural phenomena such as earthquakes, as well as some man-made hazards and etc. The problem of measuring and accounting for the scattering power of the earth's surface in the short-range of radio waves is important for a number of purposes, such as diagnosing properties of the medium, which is of interest for geological, environmental studies. In this paper, we propose a new method for estimating the parameters of incoherent signal/noise ratio. The paper presents the results of comparison of the measurement method from the point of view of their admissible relative analytical errors. The new method is suggested. Analysis of analytical error of estimation of this parameter allowed to recommend new method instead of standard method. A comparative analysis and shows that the analytical (relative) accuracy of the determination of this parameter new method on the order exceeds the widely-used standard method.

This method enables sensing and quantization of analog strain gauges. By manufacturing a piezoelectric sensor stack in parallel (physical) with a piezoelectric actuator stack, the capacitance of the sensor stack varies in exact proportion to the exertion applied by the actuator stack. This, in turn, varies the output frequency of the local sensor oscillator. The output, F(sub out), is fed to a phase detector, which is driven by a stable reference, F(sub ref). The output of the phase detector is a square waveform, D(sub out), whose duty cycle, t(sub W), varies in exact proportion according to whether F(sub out) is higher or lower than F(sub ref). In this design, should F(sub out) be precisely equal to F(sub ref), then the waveform has an exact 50/50 duty cycle. The waveform, D(sub out), is of generally very low frequency suitable for safe transmission over long distances without corruption. The active portion of the waveform, t(sub W), gates a remotely located counter, which is driven by a stable oscillator (source) of such frequency as to give sufficient digitization of t(sub W) to the resolution required by the application. The advantage to this scheme is that it negates the most-common, present method of sending either very low level signals (viz. direct output from the sensors) across great distances (anything over one-half meter) or the need to transmit widely varying higher frequencies over significant distances thereby eliminating interference [both in terms of beat frequency generation and in-situ EMI (electromagnetic interference)] caused by ineffective shielding. It also results in a significant reduction in shielding mass.

Airplane detection in remotesensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remotesensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

The invasion of the wild oyster Crassostrea gigas along the western European Atlantic coast has generated changes in the structure and functioning of intertidal ecosystems. Considered as an invasive species and a trophic competitor of the cultivated conspecific oyster, it is now seen as a resource by oyster farmers following recurrent mass summer mortalities of oyster spat since 2008. Spatial distribution maps of wild oyster reefs are required by local authorities to help define management strategies. In this work, visible-near infrared (VNIR) hyperspectral and multispectral remotesensing was investigated to map two contrasted intertidal reef structures: clusters of vertical oysters building three-dimensional dense reefs in muddy areas and oysters growing horizontally creating large flat reefs in rocky areas. A spectral library, collected in situ for various conditions with an ASD spectroradiometer, was used to run Spectral Angle Mapper classifications on airborne data obtained with an HySpex sensor (160 spectral bands) and SPOT satellite HRG multispectral data (3 spectral bands). With HySpex spectral/spatial resolution, horizontal oysters in the rocky area were correctly classified but the detection was less efficient for vertical oysters in muddy areas. Poor results were obtained with the multispectral image and from spatially or spectrally degraded HySpex data, it was clear that the spectral resolution was more important than the spatial resolution. In fact, there was a systematic mud deposition on shells of vertical oyster reefs explaining the misclassification of 30% of pixels recognized as mud or microphytobenthos. Spatial distribution maps of oyster reefs were coupled with in situ biomass measurements to illustrate the interest of a remotesensing product to provide stock estimations of wild oyster reefs to be exploited by oyster producers. This work highlights the interest of developing remotesensing techniques for aquaculture applications in coastal

Full Text Available A mixed methods bibliometric analysis was performed to ascertain the characteristic of scientific literature published in a 10-year period (2007–2016 regarding the application of remotesensing data in human health. A search was performed on the Scopus database, followed by manual revision using synthesis studies’ techniques, requiring the authors to sort through more than 8000 medical concepts to create the query, and to manually select relevant papers from over 2000 documents. From the initial 2752 papers identified, 520 articles were selected for analysis, showing that the United States ranked first, with a total of 250 (48.1% of the total documents, followed by France and the United Kingdom, with 67 (12.9% of the total and 54 (10.4% of the total documents, respectively. When considering authorship, the top three authors were Vounatsou P (22 articles, Utzinger J (19 articles, and Vignolles C (13 articles. Regarding disease-specific keywords, malaria, dengue, and schistosomiasis were the most frequent keywords, occurring 142, 34, and 24 times, respectively. For some infectious diseases and other highly pathogenic or emerging infectious diseases, remotesensing has become a very powerful instrument. Also, several studies relate different environmental factors retrieved by remotesensing data with other diseases, such as asthma exacerbations. Health-related remotesensing publications are increasing and this paper highlights the importance of these related technologies toward better information and, ideally, better provision of healthcare. On the other hand, this paper provides an overall picture of the state of the research regarding the application of remotesensing data in human health and identifies the most active stakeholders e.g., authors and institutions in the field, informing possible new collaboration research groups.

Remotesensing techniques enhance the selection and evaluation process for nuclear power plant siting. The principal advantage is the synoptic view which improves recognition of linear features, possibly indicative of faults. The interpretation of such images, in conjunction with seismological studies, also permits delineation of seismo-tectonic provinces. In volcanic terrains, geomorphic-age boundaries can be delineated and volcanic centers identified, providing necessary guidance for field sampling and regional model derivation. The use of such techniques is considered for studies in the Philippines, Mexico, and Greece. 5 refs

The United States Department of Energy (USDOE) maintains a RemoteSensing Laboratory (RSL) to support nuclear related programs of the US Government. The mission of the organization includes both emergency response and routine environmental assessments of nuclear facilities. The unique suite of equipment used by RSL for multisensor surveys of nuclear facilities include gamma radiation sensors, mapping quality aerial cameras, video cameras, thermal imagers, and multispectral scanners. Results for RSL multisensor surveys that have been conducted at the Savannah River Site (SRS) located in South Carolina are presented

Reliable, high-capacity communications in scattering media can be effectively established with some basic remotesensing techniques involving time reversal. I will formulate these problems and discuss the various mathematical approaches that can be used for analysis. It turns out that stochastic analysis plays an important role and, in some cases, gives very satisfactory results. One such result is the spectacular increase in communications capacity in a richly scattering environment. I will end with a discussion of applications and computational issues that arise in the realistic simulation of communication systems.

The contribution of remotesensing to environmental management procedures at the sub-regional scale is examined in relation to the County Structure environmental management plan for Merseyside County, England. The various seasons, scales and emulsions used for aerial photography in the county are indicated, and results of aerial surveys of the distribution of derelict and despoiled land and of natural environments are presented and compared with ground surveys. The use of color infrared and panchromatic aerial photographs indicating areas of environmental stress and land use in the formulation, implementation and monitoring of environmental management activities is then discussed.

Unmanned systems and robotics technologies have become very popular recently owing to their ability to replace human beings in dangerous, tedious, or repetitious jobs. This book fill the gap in the field between research and real-world applications, providing scientists and engineers with essential information on how to design and employ networked unmanned vehicles for remotesensing and distributed control purposes. Target scenarios include environmental or agricultural applications such as river/reservoir surveillance, wind profiling measurement, and monitoring/control of chemical leaks.

Full Text Available Remote-sensing-derived elevation data sets often suffer from noise and outliers due to various reasons, such as the physical limitations of sensors, multiple reflectance, occlusions and low contrast of texture. Outliers generally have a seriously negative effect on DEM construction. Some interpolation methods like ordinary kriging (OK are capable of smoothing noise inherent in sample points, but are sensitive to outliers. In this paper, a robust algorithm of multiquadric method (MQ based on an Improved Huber loss function (MQ-IH has been developed to decrease the impact of outliers on DEM construction. Theoretically, the improved Huber loss function is null for outliers, quadratic for small errors, and linear for others. Simulated data sets drawn from a mathematical surface with different error distributions were employed to analyze the robustness of MQ-IH. Results indicate that MQ-IH obtains a good balance between efficiency and robustness. Namely, the performance of MQ-IH is comparative to those of the classical MQ and MQ based on the Classical Huber loss function (MQ-CH when sample points follow a normal distribution, and the former outperforms the latter two when sample points are subject to outliers. For example, for the Cauchy error distribution with the location parameter of 0 and scale parameter of 1, the root mean square errors (RMSEs of MQ-CH and the classical MQ are 0.3916 and 1.4591, respectively, whereas that of MQ-IH is 0.3698. The performance of MQ-IH is further evaluated by qualitative and quantitative analysis through a real-world example of DEM construction with the stereo-images-derived elevation points. Results demonstrate that compared with the classical interpolation methods, including natural neighbor (NN, OK and ANUDEM (a program that calculates regular grid digital elevation models (DEMs with sensible shape and drainage structure from arbitrarily large topographic data sets, and two versions of MQ, including the

A low-power shock sensing system includes at least one shock sensor physically coupled to a chemical storage tank to be monitored for impacts, and an RF transmitter which is in a low-power idle state in the absence of a triggering signal. The system includes interference circuitry including or activated by the shock sensor, wherein an output of the interface circuitry is coupled to an input of the RF transmitter. The interface circuitry triggers the RF transmitting with the triggering signal to transmit an alarm message to at least one remote location when the sensor senses a shock greater than a predetermined threshold. In one embodiment the shock sensor is a shock switch which provides an open and a closed state, the open state being a low power idle state.

It is noted that within many geography departments remotesensing is viewed as a mere technique a student should learn in order to carry out true geographic research. This view inhibits both students and faculty from investigation of remotelysensed data as a new source of geographic knowledge that may alter our understanding of the Earth. The tendency is for geographers to accept these new data and analysis techniques from engineers and mathematicians without questioning the accompanying premises. This black-box approach hinders geographic applications of the new remotelysensed data and limits the geographer's contribution to further development of remotesensing observation systems. It is suggested that geographers contribute to the development of remotesensing through pursuit of basic research. This research can be encouraged, particularly among students, by demonstrating the links between geographic theory and remotelysensed observations, encouraging a healthy skepticism concerning the current understanding of these data.

The content of typical basic and advanced remotesensing and image interpretation courses are described and typical remotesensing graduate programs of study in civil engineering and in interdisciplinary environmental remotesensing and water resources management programs are outlined. Ideally, graduate programs with an emphasis on remotesensing and image interpretation should be built around a core of five courses: (1) a basic course in fundamentals of remotesensing upon which the more specialized advanced remotesensing courses can build; (2) a course dealing with visual image interpretation; (3) a course dealing with quantitative (computer-based) image interpretation; (4) a basic photogrammetry course; and (5) a basic surveying course. These five courses comprise up to one-half of the course work required for the M.S. degree. The nature of other course work and thesis requirements vary greatly, depending on the department in which the degree is being awarded.

These proceedings contain papers presented at the Eighth Thematic Conference on Geologic RemoteSensing. This meeting was held April 29-May 2, 1991, in Denver, Colorado, USA. The conference was organized by the Environmental Research Institute of Michigan, in Cooperation with an international program committee composed primarily of geologic remotesensing specialists. The meeting was convened to discuss state-of-the-art exploration, engineering, and environmental applications of geologic remotesensing as well as research and development activities aimed at increasing the future capabilities of this technology. The presentations in these volumes address the following topics: Spectral Geology; U.S. and International Hydrocarbon Exploration; Radar and Thermal Infrared RemoteSensing; Engineering Geology and Hydrogeology; Minerals Exploration; RemoteSensing for Marine and Environmental Applications; Image Processing and Analysis; Geobotanical RemoteSensing; Data Integration and Geographic Information Systems

A pilot program carried out in Western Canada to test remotesensing under semi-operational conditions and display its applicability to operational range management programs was described. Four agencies were involved in the program, two in Alberta and two in Manitoba. Each had different objectives and needs for remotesensing within its range management programs, and each was generally unfamiliar with remotesensing techniques and their applications. Personnel with experience and expertise in the remotesensing and range management fields worked with the agency personnel through every phase of the pilot program. Results indicate that these agencies have found remotesensing to be a cost effective tool and will begin to utilize remotesensing in their operational work during ensuing seasons.

Plane is an important target category in remotesensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remotesensing image has been very high and we can get more detailed information for detecting the remotesensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remotesensing target detection and proposed an algorithm with end to end deep network, which can learn from the remotesensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

Conference (PORSEC), earlier known as the Paci c Ocean RemoteSensing Conference (PORSEC), was formed in 1992 to provide a venue for international cooperation in the increasingly important area of remotesensing of the ocean. Many countries that border... and ocean dynamics, and modeling with satellite sensor (mainly microwave) data. Some of the presentations are of regional interest, while others will nd an audience beyond the satellite remotesensing community. These rst results through their simple...

The Arctic Institute of North America long has been interested in encouraging full and specific attention to applications of remotesensing to polar...research problems. The major purpose of the symposium was to acquaint scientists and technicians concerned with remotesensing with some of the...special problems of the polar areas and, in turn, to acquaint polar scientists with the potential of the use of remotesensing . The Symposium therefore was

Full Text Available When observing the Earth from above at night, it is clear that the human settlement and major economic regions emit glorious light. At cloud-free nights, some remotesensing satellites can record visible radiance source, including city light, fishing boat light and fire, and these nighttime cloud-free images are remotelysensed nighttime light images. Different from daytime remotesensing, nighttime light remotesensing provides a unique perspective on human social activities, thus it has been widely used for spatial data mining of socioeconomic domains. Historically, researches on nighttime light remotesensing mostly focus on urban land cover and urban expansion mapping using DMSP/OLS imagery, but the nighttime light images are not the unique remotesensing source to do these works. Through decades of development of nighttime light product, the nighttime light remotesensing application has been extended to numerous interesting and scientific study domains such as econometrics, poverty estimation, light pollution, fishery and armed conflict. Among the application cases, it is surprising to see the Gross Domestic Production (GDP data can be corrected using the nighttime light data, and it is interesting to see mechanism of several diseases can be revealed by nighttime light images, while nighttime light are the unique remotesensing source to do the above works. As the nighttime light remotesensing has numerous applications, it is important to summarize the application of nighttime light remotesensing and its data mining fields. This paper introduced major satellite platform and sensors for observing nighttime light at first. Consequently, the paper summarized the progress of nighttime light remotesensing data mining in socioeconomic parameter estimation, urbanization monitoring, important event evaluation, environmental and healthy effects, fishery dynamic mapping, epidemiological research and natural gas flaring monitoring. Finally, future

A research project, aiming at investigation the use of remotesensing in uranium exploration, has been accomplished on data from South Greenland. During the project, analyses have been done on pure remotesensing data (Landsat MSS) and on integrated data of various types, including geochemical, aeromagnetic, radiometric and geological data in addition to the MSS data. Ratioing, factor analysis and discriminant analysis were used for enhancement of colour anomalies which correspond to oxidation zones. Some of the anomalies coincide with U and Nb mineralizations. Lineaments were mapped visually from photoprints, digitized and analysed statistically. A sinusoidal model could be applied to the general directional frequency distribution and was used to define ten classes of significant directions. Three of these directions were of major geological significance. Thus some of the major alkaline intrusions are situated at the intersections of some of the lineaments, a particular NE-SW trending lineament coincides with a geochemical boundary and pitchblende occurrences may be related to a WNW-ESE direction. The various types of data set were brought onto format of the Landsat images and collected in a data base. Representing three different types of data (Landsat MSS-band 7, aeromagnetic data and the geochemical Fe-content of stream sediments) on basis of intensity, hue and saturation revealed new features among which can be mentioned a possible indication of a subsurface continuation of one of the major alkaline intrusions. (author)

This paper presents a study for linking remotelysensed data with property tax related issues. First, it discusses the key attributes required for property taxation and evaluates the capabilities of remotesensing technology to measure these attributes accurately at parcel level. Next, it presents a detailed case study of six representative wards of different characteristics in Dehradun, India, that illustrates how measurements of several of these attributes supported by field survey can be combined to address the issues related to property taxation. Information derived for various factors quantifies the property taxation contributed by an average dwelling unit of the different income groups. Results show that the property tax calculated in different wards varies between 55% for the high-income group, 32% for the middle-income group, 12% for the low-income group and 1% for squatter units. The study concludes that higher spatial resolution satellite data and integrates social survey helps to assess the socio-economic status of the population for tax contribution purposes.

Particulate matter air pollution is a ubiquitous exposure linked with multiple adverse health outcomes for children and across the life course. The recent development of satellite-based remote-sensing models for air pollution enables the quantification of these risks and addresses many limitations of previous air pollution research strategies. We review the recent literature on the applications of satellite remotesensing in air quality research, with a focus on their use in epidemiological studies. Aerosol optical depth (AOD) is a focus of this review and a significant number of studies show that ground-level particulate matter can be estimated from columnar AOD. Satellite measurements have been found to be an important source of data for particulate matter model-based exposure estimates, and recently have been used in health studies to increase the spatial breadth and temporal resolution of these estimates. It is suggested that satellite-based models improve our understanding of the spatial characteristics of air quality. Although the adoption of satellite-based measures of air quality in health studies is in its infancy, it is rapidly growing. Nevertheless, further investigation is still needed in order to have a better understanding of the AOD contribution to these prediction models in order to use them with higher accuracy in epidemiological studies.

The current offer by the United States Department of Commerce to transfer the U.S. land remotesensing program to the private sector is described. A Request for Proposals (RFP) was issued, soliciting offers from U.S. firms to provide a commercial land remotesensing satellite system. Proposals must address a complete system including satellite, communications, and ground data processing systems. Offerors are encouraged to propose to take over the Government LANDSAT system which consists of LANDSAT 4 and LANDSAT D'. Also required in proposals are the market development procedures and plans to ensure that commercialization is feasible and the business will become self-supporting at the earliest possible time. As a matter of Federal Policy, the solicitation is designed to protect both national security and foreign policy considerations. In keeping with these concerns, an offeror must be a U.S. Firm. Requirements for data quality, quantity, distribution and delivery are met by current operational procedures. It is the Government's desire that the Offeror be prepared to develop and operate follow-on systems without Government subsidies. However, to facilitate rapid commercialization, an offeror may elect to include in his proposal mechanisms for short term government financial assistance.

In an attempt to determine the ability of remotesensing techniques to economically generate data required by water demand models, the Geography RemoteSensing Unit, in conjunction with the Kern County Water Agency of California, developed an analysis model. As a result it was determined that agricultural cropland inventories utilizing both high altitude photography and LANDSAT imagery can be conducted cost effectively. In addition, by using average irrigation application rates in conjunction with cropland data, estimates of agricultural water demand can be generated. However, more accurate estimates are possible if crop type, acreage, and crop specific application rates are employed. An analysis of the effect of saline-alkali soils on water demand in the study area is also examined. Finally, reference is made to the detection and delineation of water tables that are perched near the surface by semi-permeable clay layers. Soil salinity prediction, automated crop identification on a by-field basis, and a potential input to the determination of zones of equal benefit taxation are briefly touched upon.

Full Text Available Image registration is a basic but essential step for remotesensing image processing, and finding stable features in multitemporal images is one of the most considerable challenges in the field. The main shape contours of artificial objects (e.g., roads, buildings, farmlands, and airports can be generally described as a group of line segments, which are stable features, even in images with evident background changes (e.g., images taken before and after a disaster. In this study, a registration method that uses line segments and their intersections is proposed for multitemporal remotesensing images. First, line segments are extracted in image pyramids to unify the scales of the reference image and the test image. Then, a line descriptor based on the gradient distribution of local areas is constructed, and the segments are matched in image pyramids. Lastly, triplets of intersections of matching lines are selected to estimate affine transformation between two images. Additional corresponding intersections are provided based on the estimated transformation, and an iterative process is adopted to remove outliers. The performance of the proposed method is tested on a variety of optical remotesensing image pairs, including synthetic and real data. Compared with existing methods, our method can provide more accurate registration results, even in images with significant background changes.

Context- and project-based teaching has proven to foster different affective and cognitive aspects of learning. As a versatile and multidisciplinary scientific research area with diverse applications for everyday life, satellite remotesensing is an interesting context for physics education. In this paper we give a brief overview of satellite remotesensing of vegetation and how to obtain your own, individual infrared remotesensing data with affordable converted digital cameras. This novel technique provides the opportunity to conduct individual remotesensing measurement projects with students in their respective environment. The data can be compared to real satellite data and is of sufficient accuracy for educational purposes.

Selected applications of orbital remotesensing to water resources undertaken by INPE are described. General specifications of Earth application satellites and technical characteristics of LANDSAT 1, 2, 3, and 4 subsystems are described. Spatial, temporal and spectral image attributes of water as well as methods of image analysis for applications to water resources are discussed. Selected examples are referred to flood monitoring, analysis of water suspended sediments, spatial distribution of pollutants, inventory of surface water bodies and mapping of alluvial aquifers.

Remotesensing image segmentation is a key technology for processing remotesensing images. The image segmentation results can be used for feature extraction, target identification and object description. Thus, image segmentation directly affects the subsequent processing results. This paper proposes a novel Optimum-Path Forest (OPF) clustering algorithm that can be used for remotesensing segmentation. The method utilizes the principle that the cluster centres are characterized based on their densities and the distances between the centres and samples with higher densities. A new OPF clustering algorithm probability density function is defined based on this principle and applied to remotesensing image segmentation. Experiments are conducted using five remotesensing land cover images. The experimental results illustrate that the proposed method can outperform the original OPF approach.

Computing Methods of fractal dimension and multifractal spectrum about the remotesensing image are briefly introduced. The fractal method is used to study the characteristics of remotesensing images in Xiangshan and Yuhuashan volcanic uranium metallogenic areas in southern China. The research results indicate that the Xiangshan basin in which lots of volcanic uranium deposits occur,is of bigger fractal dimension based on remotesensing image texture than that of the Yuhuashan basin in which two uranium ore occurrences exist, and the multifractal spectrum in the Xiangshan basin obviously leans to less singularity index than in the Yuhuashan basin. The relation of the fractal dimension and multifractal singularity of remotesensing image to uranium metallogeny are discussed. The fractal dimension and multifractal singularity index of remotesensing image may be used to predict the volcanic uranium metallogenic areas. (authors)

Cloud cover is inevitable in optical remotesensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation. To maintain the coherence of structures, structure sparsity was brought in to encourage first filling-in of missing patches on image structures. The optimization model of patch inpainting was formulated under the adaptive neighborhood-consistency constraint, which was solved by a modified orthogonal matching pursuit (OMP) algorithm. In light of these ideas, the thick-cloud removal scheme was designed and applied to images with simulated and true clouds. Comparisons and experiments show that our method can not only keep structures and textures consistent with the surrounding ground information, but also yield rare smoothing effect and block effect, which is more suitable for the removal of clouds from high-spatial resolution RS imagery with salient structures and abundant textured features.

Full Text Available Objective and effective image quality assessment (IQA is directly related to the application of optical remotesensing images (ORSI. In this study, a new IQA method of standardizing the target object recognition rate (ORR is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.

Objective and effective image quality assessment (IQA) is directly related to the application of optical remotesensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.

Subsurface and surface coalfires are a serious problem in many coal-producing countries. Combustion can occur within the coal seams (underground or surface), in piles of stored coal, or in spoil dumps at the surface. While consuming a non renewable energy source, coalfires promote several environmental problems. Among all GHGs that are emitted from coalfires, CO2 is the most significant because of its high quantity. In connection to this environmental problem, the core aim of the present research is to develop a hyperspectral remotesensing and radiative transfer based model that is able to estimate CO2 concentration (ppmv) from coalfires. Since 1960s remotesensing is being used as a tool to detect and monitoring coalfires. With time, remotesensing has proven a reliable tool to identify and monitor coalfires. In the present study multi-temporal, multi-sensor and multi-spectral thermal remotesensing data are being used to detect and monitor coalfires. Unlike the earlier studies, the present study explores the possibilities of satellite derived emissivity to detect and monitor coalfires. Two methods of emissivity extraction from satellite data were tested, namely NDVI (Normalized Difference Vegetation Index) derived and TES (Temperature emissivity separation) in two study areas situated in India and China and it was observed that the satellite derived emissivity offers a better kinetic surface temperature of the surface to understand the spread and extent of the coalfires more effectively. In order to reduce coalfire related GHG emissions and to achieve more effective fire fighting plans it is crucial to understand the dynamics of coalfire. Multitemporal spaceborne remotesensing data can be used to study the migration and expresses the results as vectors, indicating direction and speed of migration. The present study proposes a 2D model that recognizes an initiation point of coalfire from thermal remotesensing data and considers local geological settings to

Subsurface and surface coalfires are a serious problem in many coal-producing countries. Combustion can occur within the coal seams (underground or surface), in piles of stored coal, or in spoil dumps at the surface. While consuming a non renewable energy source, coalfires promote several environmental problems. Among all GHGs that are emitted from coalfires, CO2 is the most significant because of its high quantity. In connection to this environmental problem, the core aim of the present research is to develop a hyperspectral remotesensing and radiative transfer based model that is able to estimate CO2 concentration (ppmv) from coalfires. Since 1960s remotesensing is being used as a tool to detect and monitoring coalfires. With time, remotesensing has proven a reliable tool to identify and monitor coalfires. In the present study multi-temporal, multi-sensor and multi-spectral thermal remotesensing data are being used to detect and monitor coalfires. Unlike the earlier studies, the present study explores the possibilities of satellite derived emissivity to detect and monitor coalfires. Two methods of emissivity extraction from satellite data were tested, namely NDVI (Normalized Difference Vegetation Index) derived and TES (Temperature emissivity separation) in two study areas situated in India and China and it was observed that the satellite derived emissivity offers a better kinetic surface temperature of the surface to understand the spread and extent of the coalfires more effectively. In order to reduce coalfire related GHG emissions and to achieve more effective fire fighting plans it is crucial to understand the dynamics of coalfire. Multitemporal spaceborne remotesensing data can be used to study the migration and expresses the results as vectors, indicating direction and speed of migration. The present study proposes a 2D model that recognizes an initiation point of coalfire from thermal remotesensing data and considers local geological settings to

The main objective of the paper is to illustrate the potential of remotesensing data in the study and monitoring of environmental changes in western Sudan where considerable part of the area is under rangeland use. Data from NOAA satellite AVHRR sensor as well as thematic mapper Tm was used to assess the environment of the area during 1982-1997. The AVHRR data was processed into vegetation index (NDVI) images. Image analysis and classification was done using image display and analysis (IDA) GIS method to study vegetation condition in time series. The obtained information from field observations. The result showed high correlation between the information the work concluded the followings: NDVI images and thematic mapper data proved to be efficient in environment change analysis. NOAA AVHRR satellite data can provide an early-warning indicator of an approaching disaster. Remotesensing integrated into a GIS can contribute effectively to improve land management through better understanding of environment variability.(Author)

Subsurface and surface coalfires are a serious problem in many coal-producing countries. Combustion can occur within the coal seams (underground or surface), in piles of stored coal, or in spoil dumps at the surface. While consuming a non renewable energy source, coalfires promote several environmental problems. Among all GHGs that are emitted from coalfires, CO2 is the most significant because of its high quantity. In connection to this environmental problem, the core aim of the present research is to develop a hyperspectral remotesensing and radiative transfer based model that is able to estimate CO2 concentration (ppmv) from coalfires. Since 1960s remotesensing is being used as a tool to detect and monitoring coalfires. With time, remotesensing has proven a reliable tool to identify and monitor coalfires. In the present study multi-temporal, multi-sensor and multi-spectral thermal remotesensing data are being used to detect and monitor coalfires. Unlike the earlier studies, the present study explores the possibilities of satellite derived emissivity to detect and monitor coalfires. Two methods of emissivity extraction from satellite data were tested, namely NDVI (Normalized Difference Vegetation Index) derived and TES (Temperature emissivity separation) in two study areas situated in India and China and it was observed that the satellite derived emissivity offers a better kinetic surface temperature of the surface to understand the spread and extent of the coalfires more effectively. In order to reduce coalfire related GHG emissions and to achieve more effective fire fighting plans it is crucial to understand the dynamics of coalfire. Multitemporal spaceborne remotesensing data can be used to study the migration and expresses the results as vectors, indicating direction and speed of migration. The present study proposes a 2D model that recognizes an initiation point of coalfire from thermal remotesensing data and considers local geological settings to

Surface soil moisture is a key variable to describe the water and energy exchanges at the land surface/atmosphere interface. However, soil moisture is highly variable both spatially and temporally. Passive microwave remotelysensed data have great potential for providing estimates of soil moisture with good temporal repetition (on a daily basis) and at regional scale (∼ 10 km). This paper reviews the various methods for remotesensing of soil moisture from microwave radiometric systems. Potential applications from both airborne and spatial observations are discussed in the fields of agronomy, hydrology and meteorology. Emphasis in this paper is given to relatively new aspects of microwave techniques and of temporal soil moisture information analysis. In particular, the aperture synthesis technique allows us now to a address the soil moisture information needs on a global basis, from space instruments. (author) [fr

Despite a decade of progress in the field of fluvial remotesensing, there are few published works using this new technology to advance and explore fundamental ideas and theories in fluvial geomorphology. This paper will apply remotesensingmethods in order to re-visit a classic concept in fluvial geomorphology: flow resistance. Classic flow resistance equations such as those of Strickler and Keulegan typically use channel slope, channel depth or hydraulic radius and some measure channel roughness usually equated to the 50th or 84th percentile of the bed material size distribution. In this classic literature, empirical equations such as power laws are usually calibrated and validated with a maximum of a few hundred data points. In contrast, fluvial remotesensingmethods are now capable of delivering millions of high resolution data points in continuous, catchment scale, surveys. On the river Tromie in Scotland, a full dataset or river characteristics is now available. Based on low altitude imagery and NextMap topographic data, this dataset has a continuous sampling of channel width at a resolution of 3cm, of depth and median grain size at a resolution of 1m, and of slope at a resolution of 5m. This entire data set is systematic and continuous for the entire 20km length of the river. When combined with discharge at the time of data acquisition, this new dataset offers the opportunity to re-examine flow resistance equations with a 2-4 orders of magnitude increase in calibration data. This paper will therefore re-examine the classic approaches of Strickler and Keulagan along with other more recent flow resistance equations. Ultimately, accurate predictions of flow resistance from remotelysensed parameters could lead to acceptable predictions of velocity. Such a usage of classic equations to predict velocity could allow lotic habitat models to account for microhabitat velocity at catchment scales without the recourse to advanced and computationally intensive

Using satellite imagery to quantify the spatial patterns of cyanobacterial toxins has several challenges. These challenges include the need for surrogate pigments – since cyanotoxins cannot be directly detected by remotesensing, the variability in the relationship between the pigments and cyanotoxins – especially microcystins (MC), and the lack of standardization of the various measurement methods. A dual-model strategy can provide an approach to address these challenges. One model uses either chlorophyll-a (Chl-a) or phycocyanin (PC) collected in situ as a surrogate to estimate the MC concentration. The other uses a remotesensing algorithm to estimate the concentration of the surrogate pigment. Where blooms are mixtures of cyanobacteria and eukaryotic algae, PC should be the preferred surrogate to Chl-a. Where cyanobacteria dominate, Chl-a is a better surrogate than PC for remotesensing. Phycocyanin is less sensitive to detection by optical remotesensing, it is less frequently measured, PC laboratory methods are still not standardized, and PC has greater intracellular variability. Either pigment should not be presumed to have a fixed relationship with MC for any water body. The MC-pigment relationship can be valid over weeks, but have considerable intra- and inter-annual variability due to changes in the amount of MC produced relative to cyanobacterial biomass. To detect pigments by satellite, three classes of algorithms (analytic, semi-analytic, and derivative) have been used. Analytical and semi-analytical algorithms are more sensitive but less robust than derivatives because they depend on accurate atmospheric correction; as a result derivatives are more commonly used. Derivatives can estimate Chl-a concentration, and research suggests they can detect and possibly quantify PC. Derivative algorithms, however, need to be standardized in order to evaluate the reproducibility of parameterizations between lakes. A strategy for producing useful estimates

Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.

Remotesensing of agricultural land permits crop classification and mensuration which can lead to improved forecasts of production. This technique is particularly important for nations which do not already have an accurate agricultural reporting system. Better forecasts have important economic effects. International grain traders can make better decisions about when to store, buy, and sell. Farmers can make better planting decisions by taking advantage of production estimates for areas out of phase with their own agricultural calendar. World economic benefits will accrue to both buyers and sellers because of increased food supply and price stabilization. This paper reviews the econometric models used to establish this scenario and estimates the dollar value of benefits for world wheat as 200 million dollars annually for the United States and 300 to 400 million dollars annually for the rest of the world.

It is shown that satellite remotesensing provides timely and cost-effective information for siting and site evaluation of nuclear power plants. Side-looking airborne radar (SLAR) imagery is especially valuable in regions of prolonged cloud cover and haze, and provides additional assurance in siting and licensing. In addition, a wide range of enhancement techniques should be employed and different types of image should be color-combined to provide structural and lithologic information. Coastal water circulation can also be studied through repetitive coverage and the inherently synoptic nature of imaging satellites. Among the issues discussed are snow cover, sun angle, and cloud cover, and actual site evaluation studies in the Bataan peninsula of the Philippines and Laguna Verde, California

Knowledge of the emission source strengths of different (particulate and gaseous) atmospheric constituents is one of the principal ingredients upon which the modeling and forecasting of their distribution and impacts depend. Biomass burning emissions are complex and difficult to quantify. However, satellite remotesensing is providing us tremendous opportunities to measure the fire radiative energy (FRE) release rate or power (FRP), which has a direct relationship with the rates of biomass consumption and emissions of major smoke constituents. In this presentation, we will show how the satellite measurement of FRP is facilitating the quantitative characterization of biomass burning and smoke emission rates, and the implications of this unique capability for improving our understanding of smoke impacts on air quality, weather, and climate. We will also discuss some of the challenges and uncertainties associated with satellite measurement of FRP and how they are being addressed.

To move from data to information in almost all science and defense applications requires a human-in-the-loop to validate information products, resolve inconsistencies, and account for incomplete and potentially deceptive sources of information. This is a key motivation for visual analytics which aims to develop techniques that complement and empower human users. By contrast, the vast majority of algorithms developed in machine learning aim to replace human users in data exploitation. In this paper we describe a recently introduced machine learning problem, called rare category detection, which may be a better match to visual analytic environments. We describe a new design criteria for this problem, and present comparisons to existing techniques with both synthetic and real-world datasets. We conclude by describing an application in broad-area search of remotesensing imagery.

The unmanned airborne (UAV) laser spectrum radar has played a leading role in remotesensing because the transmitter and the receiver are together at laser spectrum radar. The advantages of the integrated transceiver laser spectrum radar is that it can be used in the oil and gas pipeline leak detection patrol line which needs the non-contact reflective detection. The UAV laser spectrum radar can patrol the line and specially detect the swept the area are now in no man's land because most of the oil and gas pipelines are in no man's land. It can save labor costs compared to the manned aircraft and ensure the safety of the pilots. The UAV laser spectrum radar can be also applied in the post disaster relief which detects the gas composition before the firefighters entering the scene of the rescue.

To move from data to information in almost all science and defense applications requires a human-in-the-loop to validate information products, resolve inconsistencies, and account for incomplete and potentially deceptive sources of information. This is a key motivation for visual analytics which aims to develop techniques that complement and empower human users. By contrast, the vast majority of algorithms developed in machine learning aim to replace human users in data exploitation. In this paper we describe a recently introduced machine learning problem, called rare category detection, which may be a better match to visual analytic environments. We describe a new design criteria for this problem, and present comparisons to existing techniques with both synthetic and real-world datasets. We conclude by describing an application in broad-area search of remotesensing imagery.

The availability of high spatial resolution remotesensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remotesensing image, Also Ground objects in the high resolution remotesensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remotesensing analysis methodology is widely accepted and applied in high resolution remotesensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remotesensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remotesensing image.

Full Text Available Earth observation (EO data is effective in monitoring agricultural cropping activity over large areas. An example of such an application is the GeoTerraImage crop type classification for the South African Crop Estimates Committee (CEC. The satellite based classification of crop types in South Africa provides a large scale, spatial and historical record of agricultural practices in the main crop growing areas. The results from these classifications provides data for the analysis of trends over time, in order to extract valuable information that can aid decision making in the agricultural sector. Crop cultivation practices change over time as farmers adapt to demand, exchange rate and new technology. Through the use of remotesensing, grain crop types have been identified at field level since 2008, providing a historical data set of cropping activity for the three most important grain producing provinces of Mpumalanga, Freestate and North West province in South Africa. This historical information allows the analysis of farm management practices to identify changes and trends in crop rotation and irrigation practices. Analysis of crop type classification over time highlighted practices such as: frequency of cultivation of the same crop on a field, intensified cultivation on centre pivot irrigated fields with double cropping of a winter grain followed by a summer grain in the same year and increasing cultivation of certain types of crops over time such as soyabeans. All these practices can be analysed in a quantitative spatial and temporal manner through the use of the remotesensing based crop type classifications.

Beginning in 2004, NASA has supported the development of an international network of ground-based remotesensing installations for the measurement of greenhouse gas columns. This collaboration has been successful and is currently used in both carbon cycle investigations and in the efforts to validate the GOSAT space-based column observations of CO2 and CH4. With the support of a grant, this research group has established a network of ground-based column observations that provide an essential link between the satellite observations of CO2, CO, and CH4 and the extensive global in situ surface network. The Total Carbon Column Observing Network (TCCON) was established in 2004. At the time of this report seven sites, employing modern instrumentation, were operational or were expected to be shortly. TCCON is expected to expand. In addition to providing the most direct means of tying the in situ and remotesensing data sets together, TCCON provides a means of testing the retrieval algorithms of SCIAMACHY and GOSAT over the broadest variation in atmospheric state. TCCON provides a critically maintained and long timescale record for identification of temporal drift and spatial bias in the calibration of the space-based sensors. Finally, the global observations from TCCON are improving our understanding of how to use column observations to provide robust estimates of surface exchange of C02 and CH4 in advance of the launch of OCO and GOSAT. TCCON data are being used to better understand the impact of both regional fluxes and long-range transport on gradients in the C02 column. Such knowledge is essential for identifying the tools required to best use the space-based observations. The technical approach and methodology of retrieving greenhouse gas columns from near-IR solar spectra, data quality and process control are described. Additionally, the impact of and relevance to NASA of TCCON and satellite validation and carbon science are addressed.

The rainfall and runoff relationship becomes an intriguing issue as urbanization continues to evolve worldwide. In this paper, we developed a simulation model based on the soil conservation service curve number (SCS-CN) method to analyze the rainfall-runoff relationship in Guangzhou, a rapid growing metropolitan area in southern China. The SCS-CN method was initially developed by the Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture (USDA), and is on...

Full Text Available Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remotesensing. The paper is based on the imaging process of remotesensing system, introducing cybernetics into the field of remotesensing, establishing a space-time closed-loop control theory for the actual operation of remotesensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remotesensing platform control, sensor control, data processing control, but also in whole system of remotesensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remotesensing science will improve remotesensing science to a higher level.

Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remotesensing. The paper is based on the imaging process of remotesensing system, introducing cybernetics into the field of remotesensing, establishing a space-time closed-loop control theory for the actual operation of remotesensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remotesensing platform control, sensor control, data processing control, but also in whole system of remotesensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remotesensing science will improve remotesensing science to a higher level.

Recently, geological analysis using remotesensing data has been put into practice due to data with high spectral resolution and high spatial resolution. There has been a remarkable increase in both software and hardware of personal computer. Software is independent of hardware due to Windows. It has become easy to develop softwares. Under such situation, a portable remotesensing image processing system coping with Window 95 has been developed. Using this system, basic image processing can be conducted, and present location can be displayed on the image in real time by linking with GPS. Accordingly, it is not required to bring printed images for the field works of image processing. This system can be used instead of topographic maps for overseas surveys. Microsoft Visual C++ ver. 2.0 is used for the software. 1 fig.

In this thesis the use of microwave remotesensing to estimate soil water content is investigated. A general framework is described which is applicable to both passive and active microwave remotesensing of soil water content. The various steps necessary to estimate areal soil water content

Remotesensing technologies can provide objective, practical and cost-effective solutions for developing and maintaining REDD+ monitoring systems. This paper reviews the potential and status of available remotesensing data sources with a focus on different forest information products and synergies

Full Text Available at the coast is that it is in a permanent state of change. Remotesensing, whether from orbiting (space-borne) or air-borne platforms, can greatly assist in the task of monitoring coastal environments. In particular, remotesensing enables simultaneous or near...

Airborne remotesensing at infrared wavelengths has the potential to quantify large-fire properties related to energy release or intensity, residence time, fuel-consumption rate, rate of spread, and soil heating. Remotesensing at a high temporal rate can track fire-line outbreaks and acceleration and spotting ahead of a fire front. Yet infrared imagers and imaging...

Remotesensing observations used in offshore wind energy are described in three parts: ground-based techniques and applications, airborne techniques and applications, and satellite-based techniques and applications. Ground-based remotesensing of winds is relevant, in particular, for new large wind...

A theoretical framwork is outlined for estimating social returns from research and application of remotesensing. The approximate dollar magnitude is given of a particular application of remotesensing, namely estimates of corn production, soybeans, and wheat. Finally, some comments are made on the limitations of this procedure and on the implications of results.

Remotesensing is a principal focus of NASA's technology transfer program activity with major attention to remotesensing education the Regional Program and the University Applications Program. Relevant activities over the past five years are reviewed and perspective on future directions is presented.

We describe Giovanni, the NASA Goddard developed online visualization and analysis tool that allows users explore various phenomena without learning remotesensing data formats and downloading voluminous data. Using MODIS aerosol data as an example, we formulate an approach to the data fusion for Giovanni to further enrich online multi-sensor remotesensing data comparison and analysis.

Remotesensing, geographic information systems, and modeling have combined to produce a virtual explosion of growth in ecological investigations and applications that are explicitly spatial and temporal. Of all remotelysensed data, those acquired by landsat sensors have played the most pivotal role in spatial and temporal scaling. Modern terrestrial ecology relies on...

To most land managers, remotesensing has remained illusive, seldom allowing the manager to use it to its full potential. In contrast, the policy maker, backed by GIS laboratories and remotesensing specialists, is confronted by plausible scenarios of degradation and transformation. After intervening, he is seldom active long ...

Full Text Available After the characteristics of geodesic active contour model (GAC, Chan-Vese model(CV and local binary fitting model(LBF are analyzed, and the active contour model based on regions and edges is combined with image segmentation method based on quad-tree, a waterline extraction method based on quad-tree and multiple active contour model is proposed in this paper. Firstly, the method provides an initial contour according to quad-tree segmentation. Secondly, a new signed pressure force(SPF function based on global image statistics information of CV model and local image statistics information of LBF model has been defined, and then ,the edge stopping function(ESF is replaced by the proposed SPF function, which solves the problem such as evolution stopped in advance and excessive evolution. Finally, the selective binary and Gaussian filtering level set method is used to avoid reinitializing and regularization to improve the evolution efficiency. The experimental results show that this method can effectively extract the weak edges and serious concave edges, and owns some properties such as sub-pixel accuracy, high efficiency and reliability for waterline extraction.

Full Text Available Unmanned aerial vehicles (UAVs are suited to various remotesensing missions, such as measuring air quality. The conventional method of UAV control is by human operators. Such an approach is limited by the ability of cooperation among the operators controlling larger fleets of UAVs in a shared area. The remedy for this is to increase autonomy of the UAVs in planning their trajectories by considering other UAVs and their plans. To provide such improvement in autonomy, we need better algorithms for generating alternative trajectory variants that the UAV coordination algorithms can utilize. In this article, we define a novel family of multi-UAV sensing problems, solving task allocation of huge number of tasks (tens of thousands to a group of configurable UAVs with non-zero weight of equipped sensors (comprising the air quality measurement as well together with two base-line solvers. To solve the problem efficiently, we use an algorithm for diverse trajectory generation and integrate it with a solver for the multi-UAV coordination problem. Finally, we experimentally evaluate the multi-UAV sensing problem solver. The evaluation is done on synthetic and real-world-inspired benchmarks in a multi-UAV simulator. Results show that diverse planning is a valuable method for remotesensing applications containing multiple UAVs.

Full Text Available The capability for mapping two species of seagrass, Thalassia testudinium and Syringodium filiforme, by remotesensing using a physics based model inversion method was investigated. The model was based on a three-dimensional canopy model combined with a model for the overlying water column. The model included uncertainty propagation based on variation in leaf reflectances, canopy structure, water column properties, and the air-water interface. The uncertainty propagation enabled both a-priori predictive sensitivity analysis of potential capability and the generation of per-pixel error bars when applied to imagery. A primary aim of the work was to compare the sensitivity analysis to results achieved in a practical application using airborne hyperspectral data, to gain insight on the validity of sensitivity analyses in general. Results showed that while the sensitivity analysis predicted a weak but positive discrimination capability for species, in a practical application the relevant spectral differences were extremely small compared to discrepancies in the radiometric alignment of the model with the imagery—even though this alignment was very good. Complex interactions between spectral matching and uncertainty propagation also introduced biases. Ability to discriminate LAI was good, and comparable to previously published methods using different approaches. The main limitation in this respect was spatial alignment with the imagery with in situ data, which was heterogeneous on scales of a few meters. The results provide insight on the limitations of physics based inversion methods and seagrass mapping in general. Complex models can degrade unpredictably when radiometric alignment of the model and imagery is not perfect and incorporating uncertainties can have non-intuitive impacts on method performance. Sensitivity analyses are upper bounds to practical capability, incorporating a term for potential systematic errors in radiometric alignment may

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remotesensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remotesensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remotesensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Our results highlight the potential of integrating multiple remotesensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.

A laser long range remotesensing (LRS) program is being conducted by the United States Air Force Phillips Laboratory (AF/PL). As part of this program, AF/PL is testing the feasibility of developing a long path CO(subscript 2) laser-based DIAL system for remotesensing. In support of this program, the AF/PL has recently completed an experimental series using a 21 km slant- range path (3.05 km ASL transceiver height to 0.067 km ASL target height) at its Phillips Laboratory Air Force Maui Optical Station (AMOS) facility located on Maui, Hawaii. The dial system uses a 3-joule, (superscript 13)C isotope laser coupled into a 0.6 m diameter telescope. The atmospheric optical characterization incorporates information from an infrared scintillometer co-aligned to the laser path, atmospheric profiles from weather balloons launched from the target site, and meteorological data from ground stations at AMOS and the target site. In this paper, we report a description of the experiment configuration, a summary of the results, a summary of the atmospheric conditions and their implications to the LRS program. The capability of such a system for long-range, low-angle, slant-path remotesensing is discussed. System performance issues relating to both coherent and incoherent detection methods, atmospheric limitations, as well as, the development of advanced models to predict performance of long range scenarios are presented.

Besides empirical algorithms with the blue-green ratio, the algorithms based on fluorescence are also important and valid methods for retrieving chlorophyll-a concentration in the ocean waters, especially for Case II waters and the sea with algal blooming. This study reviews the history of initial cognitions, investigations and detailed approaches towards chlorophyll fluorescence, and then introduces the biological mechanism of fluorescence remotesensing and main spectral characteristics such as the positive correlation between fluorescence and chlorophyll concentration, the red shift phenomena. Meanwhile, there exist many influence factors that increase complexity of fluorescence remotesensing, such as fluorescence quantum yield, physiological status of various algae, substances with related optical property in the ocean, atmospheric absorption etc. Based on these cognitions, scientists have found two ways to calculate the amount of fluorescence detected by ocean color sensors: fluorescence line height and reflectance ratio. These two ways are currently the foundation for retrieval of chlorophyl l - a concentration in the ocean. As the in-situ measurements and synchronous satellite data are continuously being accumulated, the fluorescence remotesensing of chlorophyll-a concentration in Case II waters should be recognized more thoroughly and new algorithms could be expected.

Remotesensing systems can be considered today as a real alternative to classical soundings with respect to the MH (mixing height) determination. They have the basic advantage to allow continuous monitoring of the ABL (atmospheric boundary layer). Some technical issues which limit their operational use at present should be solved in the near future (frequency allocation, eye safety, costs). Taking into account specific operating conditions and the formulated-above requirements of a sounding system to be used for MH determination it becomes obvious that none of the available systems meets all of them, i.e., the `Mixing height-meter` does not exist. Therefore, reliable MH determination under a wide variety of conditions can be achieved only by integrating different instruments into a complex sounding system. The S-profiles provide a suitable data base for MH estimation from all types of remotesensing instruments. The criteria to deduce MH-values from these profiles should consider the structure type and the evolution stage of the ABL as well as the shape of the profiles. A certain kind of harmonization concerning these criteria should be achieved. MH values derived automatically from remotesensing data appear to be not yet reliable enough for direct operational use, they should be in any case critically examined by a trained analyst. Contemporary mathematical methods (wavelet transforms, fuzzy logics) are supposed to allow considerable progress in this field in the near future. (au) 19 refs.

With the advent of Google Earth, Google Maps, and Microsoft Bing Maps, high resolution satellite imagery are becoming more easily accessible than ever. It have been the case that the college students may already have wealth experiences with the high resolution satellite imagery by using these software and web services prior to any formal remotesensing education. It is obvious that the remotesensing education should be adjusted to the fact that the audience are already the customers of remotesensing products (through the use of the above mentioned services). This paper reports the use of openly available satellite imagery in an introductory-level remotesensing course in the Department of Geomatics of National Cheng Kung University as a term project. From the experience learned from the fall of 2009 and 2010, it shows that this term project has effectively aroused the students' enthusiastic toward RemoteSensing.

Remotesensing is providing voluminous data and value added information products. Electronic sensors, communication electronics, computer software, hardware, and network communications technology have matured to the point where a distributed infrastructure for remotelysensed information is a reality. The amount of remotelysensed data and information is making distributed infrastructure almost a necessity. This infrastructure provides data collection, archiving, cataloging, browsing, processing, and viewing for applications from scientific research to economic, legal, and national security decision making. The remotesensing field is entering a new exciting stage of commercial growth and expansion into the mainstream of government and business decision making. This paper overviews this new distributed infrastructure and then focuses on describing a software system for on-line catalog access and distribution of remotelysensed information.

The present activities and future missions of the ESA program of spaceborne remotesensing of earth resources and environment are discussed. Program objectives have been determined to be the satisfaction of European regional needs by agricultural, land use, water resources, coastal and polar surveys, and meeting the requirements of developing nations in the areas of agricultural production, mineral exploration and disaster warning and assessment. The Earthnet system of data processing centers presently is used for the distribution of remotesensing data acquired by NASA satellites. Remotesensing experiments to be flown aboard Spacelab are the Metric Camera, to test high resolution mapping capabilities of a large format camera, and the Microwave Remote-Sensing Experiment, which operates as a two-frequency scatterometer, a synthetic aperture radar and a passive microwave radiometer. Studies carried out on the definition of future remotesensing satellite systems are described, including studies of system concepts for land applications and coastal monitoring satellites.

Limited awareness of environmental remote sensing’s potential ability to support environmental policy development constrains the technology’s utilization. This paper reviews the potential of earth observation from the perspective of environmental policy. A literature review of “remotesensing and policy” revealed that while the number of publications in this field increased almost twice as rapidly as that of remotesensing literature as a whole (15.3 versus 8.8% yr−1), there is apparently lit...

-based method for determining thresholds for differentiating between change and no-change in the difference images, and for estimating the variance of the no-change observations. This variance is used to establish a single change/no-change image based on the general multivariate difference image. The resulting....../no-change image can be used to establish both change regions and to extract observations based on which a fully automated orthogonal regression analysis based normalization of the multivariate data between the two points in time can be developed. Also, regularization issues typically important in connection...

In a continuing effort to develop suitable methods for the surveillance of Harmful Algal Blooms (HABs) of Karenia brevis using satellite radiometers, a new multi-algorithm method was developed to explore whether improvements in the remotesensing detection of the Florida Red Tide was possible. A Hybrid Scheme was introduced that sequentially applies the optimized versions of two pre-existing satellite-based algorithms: an Empirical Approach (using water-leaving radiance as a function of chlorophyll concentration) and a Bio-optical Technique (using particulate backscatter along with chlorophyll concentration). The long-term evaluation of the new multi-algorithm method was performed using a multi-year MODIS dataset (2002 to 2006; during the boreal Summer-Fall periods – July to December) along the Central West Florida Shelf between 25.75°N and 28.25°N. Algorithm validation was done with in situ measurements of the abundances of K. brevis; cell counts ≥1.5×104 cells l−1 defined a detectable HAB. Encouraging statistical results were derived when either or both algorithms correctly flagged known samples. The majority of the valid match-ups were correctly identified (~80% of both HABs and non-blooming conditions) and few false negatives or false positives were produced (~20% of each). Additionally, most of the HAB-positive identifications in the satellite data were indeed HAB samples (positive predictive value: ~70%) and those classified as HAB-negative were almost all non-bloom cases (negative predictive value: ~86%). These results demonstrate an excellent detection capability, on average ~10% more accurate than the individual algorithms used separately. Thus, the new Hybrid Scheme could become a powerful tool for environmental monitoring of K. brevis blooms, with valuable consequences including leading to the more rapid and efficient use of ships to make in situ measurements of HABs. PMID:21037979

Soil moisture (SM) plays a fundamental role in the land-atmosphere exchange process. Spatial estimation based on multi in situ (network) data is a critical way to understand the spatial structure and variation of land surface soil moisture. Theoretically, integrating densely sampled auxiliary data spatially correlated with soil moisture into the procedure of spatial estimation can improve its accuracy. In this study, we present a novel approach to estimate the spatial pattern of soil moisture by using the BME method based on wireless sensor network data and auxiliary information from ASTER (Terra) land surface temperature measurements. For comparison, three traditional geostatistic methods were also applied: ordinary kriging (OK), which used the wireless sensor network data only, regression kriging (RK) and ordinary co-kriging (Co-OK) which both integrated the ASTER land surface temperature as a covariate. In Co-OK, LST was linearly contained in the estimator, in RK, estimator is expressed as the sum of the regression estimate and the kriged estimate of the spatially correlated residual, but in BME, the ASTER land surface temperature was first retrieved as soil moisture based on the linear regression, then, the t-distributed prediction interval (PI) of soil moisture was estimated and used as soft data in probability form. The results indicate that all three methods provide reasonable estimations. Co-OK, RK and BME can provide a more accurate spatial estimation by integrating the auxiliary information Compared to OK. RK and BME shows more obvious improvement compared to Co-OK, and even BME can perform slightly better than RK. The inherent issue of spatial estimation (overestimation in the range of low values and underestimation in the range of high values) can also be further improved in both RK and BME. We can conclude that integrating auxiliary data into spatial estimation can indeed improve the accuracy, BME and RK take better advantage of the auxiliary

Maximum cross-correlation provides a method toremotely de-ter-mine high-lyre-solved three-dimensional fields of horizontalwinds with e-las-tic li-darthrough-out large volumes of the planetaryboundary layer (PBL). This paperdetails the technique and shows comparisonsbetween elastic lidar winds, remotelysensed laser Doppler velocimeter (LDV) windprofiles, and radiosonde winds.Radiosonde wind data were acquired at Barcelona,Spain, during the BarcelonaAir-Quality Initiative (1992), and the LDVwind data were acquired at SunlandPark, New Mexico during the 1994 Border AreaAir-Quality Study. Comparisonsshow good agreement between the differentinstruments, and demonstrate the methoduseful for air pollution management at thelocal/regional scale. Elastic lidar windscould thus offer insight into aerosol andpollution transport within the PBL. Lidarwind fields might also be used to nudge orimprove initialization and evaluation ofatmospheric meteorological models.

Full Text Available Stressful situations of plants can be caused by a lack of nutrients; mechanical damages; diseases; low or high temperatures; lack of illumination; insufficient or excess humidity of the soil; soil salinization; soil pollution by oil products or heavy metals; the increased acidity of the soil; use of pesticides, herbicides, insecticides, etc.At early stages it is often difficult to detect seemingly that the plants are in stressful situations caused by adverse external factors. However, the fluorescent analysis potentially allows detection of the stressful situations of plants by deformation of laser-induced fluorescence spectra. The paper conducts experimental investigations to learn the capabilities of the laser fluorescent method to monitor plant situations at 532nm wavelength of fluorescence excitation in the stressful situations induced by improper watering (at excess of moisture in the soil and at a lack of moisture.Researches of fluorescence spectra have been conducted using a created laboratory installation. As a source to excite fluorescence radiation the second harmonica of YAG:Nd laser is used. The subsystem to record fluorescence radiation is designed using a polychromator and a highly sensitive matrix detector with the amplifier of brightness.Experimental investigations have been conducted for fast-growing and unpretentious species of plants, namely different sorts of salad.Experimental studies of laser-induced fluorescence spectra of plants for 532nm excitement wavelength show that the impact of stressful factors on a plant due to the improper watering, significantly distorts a fluorescence spectrum of plants. Influence of a stressful factor can be shown as a changing profile of a fluorescence spectrum (an identifying factor, here, is a relationship of fluorescence intensities at two wavelengths, namely 685 nm and 740 nm or (and as a changing level of fluorescence that can be the basis for the laser method for monitoring the plant

Full Text Available This paper evaluates the information content for the retrieval of key aerosol microphysical and surface properties for multispectral single-viewing satellite polarimetric measurements cantered at 410, 443, 555, 670, 865, 1610 and 2250 nm over bright land. To conduct the information content analysis, the synthetic data are simulated by the Unified Linearized Vector Radiative Transfer Model (UNLVTM with the intensity and polarization together over bare soil surface for various scenarios. Following the optimal estimation theory, a principal component analysis method is employed to reconstruct the multispectral surface reflectance from 410 nm to 2250 nm, and then integrated with a linear one-parametric BPDF model to represent the contribution of polarized surface reflectance, thus further to decouple the surface-atmosphere contribution from the TOA measurements. Focusing on two different aerosol models with the aerosol optical depth equal to 0.8 at 550 nm, the total DFS and DFS component of each retrieval aerosol and surface parameter are analysed. The DFS results show that the key aerosol microphysical properties, such as the fine- and coarse-mode columnar volume concentration, the effective radius and the real part of complex refractive index at 550 nm, could be well retrieved with the surface parameters simultaneously over bare soil surface type. The findings of this study can provide the guidance to the inversion algorithm development over bright surface land by taking full use of the single-viewing satellite polarimetric measurements.

An open-path atmospheric CO2 measurement system was built based on tunable diode laser absorption spectroscopy (TDLAS). The CO2 absorption line near 2 μm was selected, measuring the atmospheric CO2 with direct absorption spectroscopy and carrying on the comparative experiment with multipoint measuring instruments of the open-path. The detection limit of the TDLAS system is 1.94×10-6. The calibration experiment of three AZ-7752 handheld CO2 measuring instruments was carried out with the Los Gatos Research gas analyzer. The consistency of the results was good, and the handheld instrument could be used in the TDLAS system after numerical calibration. With the contrast of three AZ-7752 and their averages, the correlation coefficients are 0.8828, 0.9004, 0.9079, and 0.9393 respectively, which shows that the open-path TDLAS has the best correlation with the average of three AZ-7752 and measures the concentration of atmospheric CO2 accurately. Multipoint measurement provides a convenient comparative method for open-path TDLAS.

This paper evaluates the information content for the retrieval of key aerosol microphysical and surface properties for multispectral single-viewing satellite polarimetric measurements cantered at 410, 443, 555, 670, 865, 1610 and 2250 nm over bright land. To conduct the information content analysis, the synthetic data are simulated by the Unified Linearized Vector Radiative Transfer Model (UNLVTM) with the intensity and polarization together over bare soil surface for various scenarios. Following the optimal estimation theory, a principal component analysis method is employed to reconstruct the multispectral surface reflectance from 410 nm to 2250 nm, and then integrated with a linear one-parametric BPDF model to represent the contribution of polarized surface reflectance, thus further to decouple the surface-atmosphere contribution from the TOA measurements. Focusing on two different aerosol models with the aerosol optical depth equal to 0.8 at 550 nm, the total DFS and DFS component of each retrieval aerosol and surface parameter are analysed. The DFS results show that the key aerosol microphysical properties, such as the fine- and coarse-mode columnar volume concentration, the effective radius and the real part of complex refractive index at 550 nm, could be well retrieved with the surface parameters simultaneously over bare soil surface type. The findings of this study can provide the guidance to the inversion algorithm development over bright surface land by taking full use of the single-viewing satellite polarimetric measurements.

Full Text Available User-driven requirements for remotesensing data are difficult to define,especially details on geometric, spectral and radiometric parameters. Even more difficult isa decent assessment of the required degrees of processing and corresponding data quality. Itis therefore a real challenge to appropriately assess data costs and services to be provided.In 2006, the HYRESSA project was initiated within the framework 6 programme of theEuropean Commission to analyze the user needs of the hyperspectral remote sensingcommunity. Special focus was given to finding an answer to the key question, Ã¢Â€ÂœWhat arethe individual user requirements for hyperspectral imagery and its related data products?Ã¢Â€Â.A Value-Benefit Analysis (VBA was performed to retrieve user needs and address openitems accordingly. The VBA is an established tool for systematic problem solving bysupporting the possibility of comparing competing projects or solutions. It enablesevaluation on the basis of a multidimensional objective model and can be augmented withexpertÃ¢Â€Â™s preferences. After undergoing a VBA, the scaling method (e.g., Law ofComparative Judgment was applied for achieving the desired ranking judgments. Theresult, which is the relative value of projects with respect to a well-defined main objective,can therefore be produced analytically using a VBA. A multidimensional objective modeladhering to VBA methodology was established. Thereafter, end users and experts wererequested to fill out a Questionnaire of User Needs (QUN at the highest level of detail -the value indicator level. The end user was additionally requested to report personalpreferences for his particular research field. In the end, results from the expertsÃ¢Â€Â™ evaluationand results from a sensor data survey can be compared in order to understand user needsand the drawbacks of existing data products. The investigation Ã¢Â€Â“ focusing on the needs of the hyperspectral user

Small remotely piloted aircraft have recently been used for maritime remotesensing, including launch and retrieval operations from land, ships and sea ice. Such aircraft can also function to collect and communicate data from other ocean observing system platforms including moorings, tagged animals, drifters, autonomous surface vessels (ASVs), and autonomous underwater vessels (AUVs). The use of small remotely piloted aircraft (or UASs, unmanned aerial systems) with a combination of these capabilities will be required to monitor the vast areas of the open ocean, as well as in harsh high-latitude ecosystems. Indeed, these aircraft are a key component of planned high latitude maritime domain awareness environmental data collection capabilities, including use of visible, IR and hyperspectral sensors, as well as lidar, meteorological sensors, and interferometric synthetic aperture radars (ISARs). We here first describe at-sea demonstrations of improved reliability and bandwidth of communications from ocean sensors on autonomous underwater vehicles to autonomous surface vessels, and then via remotely piloted aircraft to shore, ships and manned aircraft using Delay and Disruption Tolerant (DTN) communication protocols. DTN enables data exchange in communications-challenged environments, such as remote regions of the ocean including high latitudes where low satellite angles and auroral disturbances can be problematic. DTN provides a network architecture and application interface structured around optionally-reliable asynchronous message forwarding, with limited expectations of end-to-end connectivity and node resources. This communications method enables aircraft and surface vessels to function as data mules to move data between physically disparate nodes. We provide examples of the uses of this communication protocol for environmental data collection and data distribution with a variety of different remotely piloted aircraft in a coastal ocean environment. Next, we

The rapid growth of commercial remotesensing has made high quality digital sensing data widely available -- now, remotesensing must become and remain a strong, commercially viable industry. However, this new industry cannot survive without an educated consumer base. To access markets, remotesensing providers must make their product more accessible, both literally and figuratively: Potential customers must be able to find the data they require, when they require it, and they must understand the utility of the information available to them. The Internet and the World Wide Web offer the perfect medium to educate potential customers and to sell remotesensing data to those customers. A well-designed web presence can provide both an information center and a market place for companies offering their data for sale. A very high potential web-based market for remotesensing lies in media. News agencies, web sites, and a host of other visual media services can use remotesensing data to provide current, relevant information regarding news around the world. This paper will provide a model for promotion and sale of remotesensing data via the Internet.

Taking remotesensing data, airborne radiometric data and aero magnetic survey data as an example, the authors elaborate about basic thinking of remotesensing data processing methods, spectral feature analysis and adopted processing methods, also explore the remotesensing data combining with the processing of airborne radiometric survey and aero magnetic survey data, and analyze geological significance of processed image. It is not only useful for geological environment research and uranium prospecting in the study area, but also reference to applications in another area. (authors)

A speech is given on operational remotesensing programs in forest management and the importance of remotesensing in forestry is emphasized. Forest service priorities in using remotesensing are outlined.

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remotesensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotelysensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remotesensing to support their work, increase collaboration between the conservation science and remotesensing communities, identify and develop new and innovative uses of remotesensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remotesensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remotesensing improve the understanding of animal movements? How can remotelysensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remotesensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remotesensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remotesensing-derived products be used to value and monitor changes in ecosystem services? How can remotesensing be used to

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remotesensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotelysensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remotesensing to support their work, increase collaboration between the conservation science and remotesensing communities, identify and develop new and innovative uses of remotesensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remotesensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remotesensing improve the understanding of animal movements? How can remotelysensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remotesensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remotesensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remotesensing-derived products be used to value and monitor changes in ecosystem services? How can remotesensing be used to

This work relates to the generic problem of remote active imaging; that is, a source illuminates a target of interest and a receiver collects the scattered light off the target to obtain an image. Conventional imaging systems consist of an imaging lens and a high-resolution detector array [e.g., a CCD (charge coupled device) array] to register the image. However, conventional imaging systems for remotesensing require high-quality optics and need to support large detector arrays and associated electronics. This results in suboptimal size, weight, and power consumption. Computational ghost imaging (CGI) is a computational alternative to this traditional imaging concept that has a very simple receiver structure. In CGI, the transmitter illuminates the target with a modulated light source. A single-pixel (bucket) detector collects the scattered light. Then, via computation (i.e., postprocessing), the receiver can reconstruct the image using the knowledge of the modulation that was projected onto the target by the transmitter. This way, one can construct a very simple receiver that, in principle, requires no lens to image a target. Ghost imaging is a transverse imaging modality that has been receiving much attention owing to a rich interconnection of novel physical characteristics and novel signal processing algorithms suitable for active computational imaging. The original ghost imaging experiments consisted of two correlated optical beams traversing distinct paths and impinging on two spatially-separated photodetectors: one beam interacts with the target and then illuminates on a single-pixel (bucket) detector that provides no spatial resolution, whereas the other beam traverses an independent path and impinges on a high-resolution camera without any interaction with the target. The term ghost imaging was coined soon after the initial experiments were reported, to emphasize the fact that by cross-correlating two photocurrents, one generates an image of the target. In

At present retrieval methods in remotesensing image database are mainly based on spatial-temporal information. The increasing amount of images to be collected by the ground station of earth observing systems emphasizes the need for database management with intelligent data retrieval capabilities. The purpose of the proposed method is to realize a new content based retrieval system for remotesensing images database with an innovative search tool based on image similarity. This methodology is quite innovative for this application, at present many systems exist for photographic images, as for example QBIC and IKONA, but they are not able to extract and describe properly remote image content. The target database is set by an archive of images originated from an X-SAR sensor (spaceborne mission, 1994). The best content descriptors, mainly texture parameters, guarantees high retrieval performances and can be extracted without losses independently of image resolution. The latter property allows DBMS (Database Management System) to process low amount of information, as in the case of quick-look images, improving time performance and memory access without reducing retrieval accuracy. The matching technique has been designed to enable image management (database population and retrieval) independently of dimensions (width and height). Local and global content descriptors are compared, during retrieval phase, with the query image and results seem to be very encouraging.

Iceland is exposed to rapid and dynamic landscape changes caused by natural processes and man-made activities, which impact and challenge the country. Fast and reliable mapping and monitoring techniques are needed on a big spatial scale. However, currently there is lack of operational advanced information processing techniques, which are needed for end-users to incorporate remotesensing (RS) data from multiple data sources. Hence, the full potential of the recent RS data explosion is not being fully exploited. The project Environmental Mapping and Monitoring of Iceland by RemoteSensing (EMMIRS) bridges the gap between advanced information processing capabilities and end-user mapping of the Icelandic environment. This is done by a multidisciplinary assessment of two selected remotesensing super sites, Hekla and Öræfajökull, which encompass many of the rapid natural and man-made landscape changes that Iceland is exposed to. An open-access benchmark repository of the two remotesensing supersites is under construction, providing high-resolution LIDAR topography and hyperspectral data for land-cover and landform classification. Furthermore, a multi-temporal and multi-source archive stretching back to 1945 allows a decadal evaluation of landscape and ecological changes for the two remotesensing super sites by the development of automated change detection techniques. The development of innovative pattern recognition and machine learning-based approaches to image classification and change detection is one of the main tasks of the EMMIRS project, aiming to extract and compute earth observation variables as automatically as possible. Ground reference data collected through a field campaign will be used to validate the implemented methods, which outputs are then inferred with geological and vegetation models. Here, preliminary results of an automatic land-cover classification based on hyperspectral image analysis are reported. Furthermore, the EMMIRS project

Under Contract Number DE-AC08-90NV10845, the DOE has funded the Desert Research Institute (DRI) to examine several aspects of remotesensing, specifically with respect to how its use might help support Environmental Restoration and Waste Management (ERWM) activities at DOE sites located throughout the country. This report represents partial fulfillment of DRI's obligations under that contract and includes a review of relevant literature associated with remotesensing studies and our evaluation and recommendation as to the applicability of various remotesensing techniques for DOE needs. With respect to DOE ERWM activities, remotesensing may be broadly defined as collecting information about a target without actually being in physical contact with the object. As the common platforms for remotesensing observations are aircraft and satellites, there exists the possibility to rapidly and efficiently collect information over DOE sites that would allow for the identification and monitoring of contamination related to present and past activities. As DOE sites cover areas ranging from tens to hundreds of square miles, remotesensing may provide an effective, efficient, and economical method in support of ERWM activities. For this review, remotesensing has been limited to methods that employ electromagnetic (EM) energy as the means of detecting and measuring target characteristics

Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study areaAbstract: Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study area and based on the ground investigation and the multi-source remotesensing data of different resolutions, the estimation models for desert vegetation coverage were built, with the precisions of different estimation methods and models compared. The results showed that with the increasing spatial resolution of remotesensing data, the precisions of the estimation models increased. The estimation precision of the models based on the high, middle-high, and middle-low resolution remotesensing data was 89.5%, 87.0%, and 84.56%, respectively, and the precisions of the remotesensing models were higher than that of vegetation index method. This study revealed the change patterns of the estimation precision of desert vegetation coverage based on different spatial resolution remotesensing data, and realized the quantitative conversion of the parameters and scales among the high, middle, and low spatial resolution remotesensing data of desert vegetation coverage, which would provide direct evidence for establishing and implementing comprehensive remotesensing monitoring scheme for the ecological restoration in the study area.

Under Contract Number DE-AC08-90NV10845, the DOE has funded the Desert Research Institute (DRI) to examine several aspects of remotesensing, specifically with respect to how its use might help support Environmental Restoration and Waste Management (ERWM) activities at DOE sites located throughout the country. This report represents partial fulfillment of DRI`s obligations under that contract and includes a review of relevant literature associated with remotesensing studies and our evaluation and recommendation as to the applicability of various remotesensing techniques for DOE needs. With respect to DOE ERWM activities, remotesensing may be broadly defined as collecting information about a target without actually being in physical contact with the object. As the common platforms for remotesensing observations are aircraft and satellites, there exists the possibility to rapidly and efficiently collect information over DOE sites that would allow for the identification and monitoring of contamination related to present and past activities. As DOE sites cover areas ranging from tens to hundreds of square miles, remotesensing may provide an effective, efficient, and economical method in support of ERWM activities. For this review, remotesensing has been limited to methods that employ electromagnetic (EM) energy as the means of detecting and measuring target characteristics.

There has been a vast improvement in access to remotelysensed data in just a few recent years. This revolution of information is the result of heavy investment in new technology by governments and industry, rapid developments in computing power and storage, and easy dissemination of data over the internet. Today, remotelysensed data are available to virtually anyone with a desktop computer. Here, we review the status of one of the most popular areas of marine remotesensing research: coral reefs. Previous reviews have focused on the ability of remotesensing to map the structure and habitat composition of coral reefs, but have neglected to consider the physical environment in which reefs occur. We provide a holistic review of what can, might, and cannot be mapped using remotesensing at this time. We cover aspects of reef structure and health but also discuss the diversity of physical environmental data such as temperature, winds, solar radiation and water quality. There have been numerous recent advances in the remotesensing of reefs and we hope that this paper enhances awareness of the diverse data sources available, and helps practitioners identify realistic objectives for remotesensing in coral reef areas

There has been a vast improvement in access to remotelysensed data in just a few recent years. This revolution of information is the result of heavy investment in new technology by governments and industry, rapid developments in computing power and storage, and easy dissemination of data over the internet. Today, remotelysensed data are available to virtually anyone with a desktop computer. Here, we review the status of one of the most popular areas of marine remotesensing research: coral reefs. Previous reviews have focused on the ability of remotesensing to map the structure and habitat composition of coral reefs, but have neglected to consider the physical environment in which reefs occur. We provide a holistic review of what can, might, and cannot be mapped using remotesensing at this time. We cover aspects of reef structure and health but also discuss the diversity of physical environmental data such as temperature, winds, solar radiation and water quality. There have been numerous recent advances in the remotesensing of reefs and we hope that this paper enhances awareness of the diverse data sources available, and helps practitioners identify realistic objectives for remotesensing in coral reef areas.

Remotesensing data has had an important role in identifying and responding to inter-annual variations in the African environment during the past three decades. As a largely agricultural region with diverse but generally limited government capacity to acquire and distribute ground observations of rainfall, temperature and other parameters, remotesensing is sometimes the only reliable measure of crop growing conditions in Africa. Thus, developing and maintaining the technical and scientific capacity to analyze and utilize satellite remotesensing data in Africa is critical to augmenting the continent's local weather/climate observation networks as well as its agricultural and natural resource development and management. The report Review of RemoteSensing Needs and Applications in Africa' has as its central goal to recommend to the US Agency for International Development an appropriate approach to support sustainable remotesensing applications at African regional remotesensing centers. The report focuses on "RS applications" to refer to the acquisition, maintenance and archiving, dissemination, distribution, analysis, and interpretation of remotesensing data, as well as the integration of interpreted data with other spatial data products. The report focuses on three primary remotesensing centers: (1) The AGRHYMET Regional Center in Niamey, Niger, created in 1974, is a specialized institute of the Permanent Interstate Committee for Drought Control in the Sahel (CILSS), with particular specialization in science and techniques applied to agricultural development, rural development, and natural resource management. (2) The Regional Centre for Maiming of Resources for Development (RCMRD) in Nairobi, Kenya, established in 1975 under the auspices of the United Nations Economic Commission for Africa and the Organization of African Unity (now the African Union), is an intergovernmental organization, with 15 member states from eastern and southern Africa. (3) The

Remotesensing science is increasingly being used to support archaeological and cultural heritage research in various ways. Satellite sensors either passive or active are currently used in a systematic basis to detect buried archaeological remains and to systematic monitor tangible heritage. In addition, airborne and low altitude systems are being used for documentation purposes. Ground surveys using remotesensing tools such as spectroradiometers and ground penetrating radars can detect variations of vegetation and soil respectively, which are linked to the presence of underground archaeological features. Education activities and training of remotesensing archaeology to young people is characterized of highly importance. Specific remotesensing tools relevant for archaeological research can be developed including web tools, small libraries, interactive learning games etc. These tools can be then combined and aligned with archaeology and cultural heritage. This can be achieved by presenting historical and pre-historical records, excavated sites or even artifacts under a "remotesensing" approach. Using such non-form educational approach, the students can be involved, ask, read, and seek to learn more about remotesensing and of course to learn about history. The paper aims to present a modern didactical concept and some examples of practical implementation of remotesensing archaeology in secondary schools in Cyprus. The idea was built upon an ongoing project (ATHENA) focused on the sue of remotesensing for archaeological research in Cyprus. Through H2020 ATHENA project, the RemoteSensing Science and Geo-Environment Research Laboratory at the Cyprus University of Technology (CUT), with the support of the National Research Council of Italy (CNR) and the German Aerospace Centre (DLR) aims to enhance its performance in all these new technologies.

The technical aspects of oil spill remotesensing are examined and the practical uses and drawbacks of each technology are given with a focus on unfolding technology. The use of visible techniques is ubiquitous, but limited to certain observational conditions and simple applications. Infrared cameras offer some potential as oil spill sensors but have several limitations. Both techniques, although limited in capability, are widely used because of their increasing economy. The laser fluorosensor uniquely detects oil on substrates that include shoreline, water, soil, plants, ice, and snow. New commercial units have come out in the last few years. Radar detects calm areas on water and thus oil on water, because oil will reduce capillary waves on a water surface given moderate winds. Radar provides a unique option for wide area surveillance, all day or night and rainy/cloudy weather. Satellite-carried radars with their frequent overpass and high spatial resolution make these day-night and all-weather sensors essential for delineating both large spills and monitoring ship and platform oil discharges. Most strategic oil spill mapping is now being carried out using radar. Slick thickness measurements have been sought for many years. The operative technique at this time is the passive microwave. New techniques for calibration and verification have made these instruments more reliable.

Remotesensing techniques developed for exploration programs can often be used to address environmental issues facing the petroleum industry. While this industry becomes increasingly more environmentally conscious, budgets remain tight, requiring any technology used in environmental applications to be cost effective, widely available and reliable. In this paper a three-fold analysis of environmental issues facing the petroleum industry concludes: major areas of concern included environmental mapping natural habitats, surface cover, change through time, pollution monitoring (hazardous wastes, oil seeps and spills on and offshore), earth hazards assessment, baseline studies, facilities sitting and crisis response. options matrices were developed plotting current and near future RS technology vs environmental concerns, and each sensor/platform combination subjectively evaluated to determine which combination could best address the problem. While presently available RS technology (both airborne and spaceborne) has significant capability toward environmental mapping, hazards detection and other concerns, the anticipated launches of ERS-1, JERS-1, Landsat-6 and other systems will provide environmentally useful data available today only from relatively expensive and local airborne surveys. Low altitude airborne surveys and ground/sea truth will continue to be critical to any quantitative studies

I utilized state the art remotesensing and GIS (Geographical Information System) techniques to study large scale biological, physical and ecological processes of coastal, nearshore, and offshore waters of Lake Michigan and Lake Superior. These processes ranged from chlorophyll alpha and primary production time series analysies in Lake Michigan to coastal stamp sand threats on Buffalo Reef in Lake Superior. I used SeaWiFS (Sea-viewing Wide Field-of-view Sensor) satellite imagery to trace various biological, chemical and optical water properties of Lake Michigan during the past decade and to investigate the collapse of early spring primary production. Using spatial analysis techniques, I was able to connect these changes to some important biological processes of the lake (quagga mussels filtration). In a separate study on Lake Superior, using LiDAR (Light Detection and Ranging) and aerial photos, we examined natural coastal erosion in Grand Traverse Bay, Michigan, and discussed a variety of geological features that influence general sediment accumulation patterns and interactions with migrating tailings from legacy mining. These sediments are moving southwesterly towards Buffalo Reef, creating a threat to the lake trout and lake whitefish breeding ground.

Full Text Available In previous attempts to identify aquatic vegetation from remotely-sensed images using classification trees (CT, the images used to apply CT models to different times or locations necessarily originated from the same satellite sensor as that from which the original images used in model development came, greatly limiting the application of CT. We have developed an effective normalization method to improve the robustness of CT models when applied to images originating from different sensors and dates. A total of 965 ground-truth samples of aquatic vegetation types were obtained in 2009 and 2010 in Taihu Lake, China. Using relevant spectral indices (SI as classifiers, we manually developed a stable CT model structure and then applied a standard CT algorithm to obtain quantitative (optimal thresholds from 2009 ground-truth data and images from Landsat7-ETM+, HJ-1B-CCD, Landsat5-TM and ALOS-AVNIR-2 sensors. Optimal CT thresholds produced average classification accuracies of 78.1%, 84.7% and 74.0% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. However, the optimal CT thresholds for different sensor images differed from each other, with an average relative variation (RV of 6.40%. We developed and evaluated three new approaches to normalizing the images. The best-performing method (Method of 0.1% index scaling normalized the SI images using tailored percentages of extreme pixel values. Using the images normalized by Method of 0.1% index scaling, CT models for a particular sensor in which thresholds were replaced by those from the models developed for images originating from other sensors provided average classification accuracies of 76.0%, 82.8% and 68.9% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. Applying the CT models developed for normalized 2009 images to 2010 images resulted in high classification (78.0%–93.3% and overall (92.0%–93.1% accuracies. Our

In this paper, an airborne remotesensing data assimilation system for China Airborne RemoteSensing System is introduced. This data assimilation system is composed of a land surface model, data assimilation algorithms, observation data and fundamental parameters forcing the land surface model. In this data assimilation system, Variable Infiltration Capacity hydrologic model is selected as the land surface model, which also serves as the main framework of the system. Three-dimensional variation algorithm, four-dimensional variation algorithms, ensemble Kalman filter and Particle filter algorithms are integrated in this system. Observation data includes ground observations and remotelysensed data. The fundamental forcing parameters include soil parameters, vegetation parameters and the meteorological data

In this thesis, the author explored multi-source management problems of remotesensing data. The main idea is to use the mosaic dataset model, and the ways of an integreted display of image and its interpretation. Based on ArcGIS and IMINT feature knowledge platform, the author used the C# and other programming tools for development work, so as to design and implement multi-source remotesensing data management system function module which is able to simply, conveniently and efficiently manage multi-source remotesensing data. (authors)

Various technologies pertaining to identifying objects of interest in remotesensing images by searching over geospatial-temporal graph representations are described herein. Graphs are constructed by representing objects in remotesensing images as nodes, and connecting nodes with undirected edges representing either distance or adjacency relationships between objects and directed edges representing changes in time. Geospatial-temporal graph searches are made computationally efficient by taking advantage of characteristics of geospatial-temporal data in remotesensing images through the application of various graph search techniques.

Full Text Available The accurate estimation of deposits adhering on insulators is critical to prevent pollution flashovers which cause huge costs worldwide. The traditional evaluation method of insulator contaminations (IC is based sparse manual in-situ measurements, resulting in insufficient spatial representativeness and poor timeliness. Filling that gap, we proposed a novel evaluation framework of IC based on remotesensing and data mining. Varieties of products derived from satellite data, such as aerosol optical depth (AOD, digital elevation model (DEM, land use and land cover and normalized difference vegetation index were obtained to estimate the severity of IC along with the necessary field investigation inventory (pollution sources, ambient atmosphere and meteorological data. Rough set theory was utilized to minimize input sets under the prerequisite that the resultant set is equivalent to the full sets in terms of the decision ability to distinguish severity levels of IC. We found that AOD, the strength of pollution source and the precipitation are the top 3 decisive factors to estimate insulator contaminations. On that basis, different classification algorithm such as mahalanobis minimum distance, support vector machine (SVM and maximum likelihood method were utilized to estimate severity levels of IC. 10-fold cross-validation was carried out to evaluate the performances of different methods. SVM yielded the best overall accuracy among three algorithms. An overall accuracy of more than 70% was witnessed, suggesting a promising application of remotesensing in power maintenance. To our knowledge, this is the first trial to introduce remotesensing and relevant data analysis technique into the estimation of electrical insulator contaminations.

As of the latest technical methods, hyperspectral remotesensing technology has been widely used in each brach of the geosciences. However, it is still a blank for using the hyperspectral remotesensing to study the active structrure. Hyperspectral remotesensing, with high spectral resolution, continuous spectrum, continuous spatial data, low cost, etc, has great potentialities in the areas of stratum division and fault identification. Blind fault identification in plains and invisible fault discrimination in loess strata are the two hot problems in the current active fault research. Thus, the study of active fault based on the hyperspectral technology has great theoretical significance and practical value. Magnetic susceptibility (MS) records could reflect the rhythm alteration of the formation. Previous study shown that MS has correlation with spectral feature. In this study, the Emaokou section, located to the northwest of the town of Huairen, in Shanxi Province, has been chosen for invisible fault study. We collected data from the Emaokou section, including spectral data, hyperspectral image, MS data. MS models based on spectral features were established and applied to the UHD185 image for MS mapping. The results shown that MS map corresponded well to the loess sequences. It can recognize the stratum which can not identity by naked eyes. Invisible fault has been found in this section, which is useful for paleoearthquake analysis. The faults act as the conduit for migration of terrestrial gases, the fault zones, especially the structurally weak zones such as inrtersections or bends of fault, may has different material composition. We take Xiadian fault for study. Several samples cross-fault were collected and these samples were measured by ASD Field Spec 3 spectrometer. Spectral classification method has been used for spectral analysis, we found that the spectrum of the fault zone have four special spectral region(550-580nm, 600-700nm, 700-800nm and 800-900nm

A remotesensing research agenda designed to expand the knowledge of the spatial distribution of species richness and its ecological determinants and to predict its response to global change is proposed. Emphasis is placed on current methods of mapping species richness of both plants and animals, hypotheses concerning the biophysical factors believed to determine patterns of species richness, and anthropogenic processes causing the accelerating rate of extinctions. It is concluded that biodiversity should be incorporated more prominently into the global change and earth system science paradigms.

Full Text Available With the rapid development of remotesensing technology, the quantity and variety of remotesensing images are growing so quickly that proactive and personalized access to data has become an inevitable trend. One of the active approaches is remotesensing image recommendation, which can offer related image products to users according to their preference. Although multiple studies on remotesensing retrieval and recommendation have been performed, most of these studies model the user profiles only from the perspective of spatial area or image features. In this paper, we propose a spatiotemporal recommendation method for remotesensing data based on the probabilistic latent topic model, which is named the Space-Time Periodic Task model (STPT. User retrieval behaviors of remotesensing images are represented as mixtures of latent tasks, which act as links between users and images. Each task is associated with the joint probability distribution of space, time and image characteristics. Meanwhile, the von Mises distribution is introduced to fit the distribution of tasks over time. Then, we adopt Gibbs sampling to learn the random variables and parameters and present the inference algorithm for our model. Experiments show that the proposed STPT model can improve the capability and efficiency of remotesensing image data services.

Remotesensing techniques development have provided the opportunity for optimizing yields in the agricultural procedure and moreover to predict the forthcoming yield. Yield prediction plays a vital role in Agricultural Policy and provides useful data to policy makers. In this context, crop and soil parameters along with NDVI index which are valuable sources of information have been elaborated statistically to test if a) Durum wheat yield can be predicted and b) when is the actual time-window to predict the yield in the district of Paphos, where Durum wheat is the basic cultivation and supports the rural economy of the area. 15 plots cultivated with Durum wheat from the Agricultural Research Institute of Cyprus for research purposes, in the area of interest, have been under observation for three years to derive the necessary data. Statistical and remotesensing techniques were then applied to derive and map a model that can predict yield of Durum wheat in this area. Indeed the semi-empirical model developed for this purpose, with very high correlation coefficient R2=0.886, has shown in practice that can predict yields very good. Students T test has revealed that predicted values and real values of yield have no statistically significant difference. The developed model can and will be further elaborated with more parameters and applied for other crops in the near future.

The Melt Area Detection Index (MADI), a remotesensing algorithm to discriminate between dry and wet snow, has been previously developed and applied to the western portion of the Greenland ice sheet for the years 2000-2006, using Moderate Resolution Imaging Radiospectrometer (MODIS) data (Chylek et al, 2007). We extend that work both spatially and temporally by taking advantage of newly available data, and developing algorithms that facilitate the sensing of cloud cover and the automated inference of wet snow regions. The automated methods allow the development of a composite melt area data product with 0.25 km^2 spatial resolution and approximately two week temporal resolution. We discuss melt area dynamics that are inferred from this high resolution composite melt area. Chylek, P., M. McCabe, M. K. Dubey, and J. Dozier (2007), Remotesensing of Greenland ice sheet using multispectral near-infrared and visible radiances, J. Geophys. Res., 112, D24S20, doi:10.1029/2007JD008742.

We illustrate the utility of variational destriping for ocean color images from both multispectral and hyperspectral sensors. In particular, we examine data from a filter spectrometer, the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar Partnership (NPP) orbiter, and an airborne grating spectrometer, the Jet Population Laboratory's (JPL) hyperspectral Portable Remote Imaging Spectrometer (PRISM) sensor. We solve the destriping problem using a variational regularization method by giving weights spatially to preserve the other features of the image during the destriping process. The target functional penalizes the neighborhood of stripes (strictly, directionally uniform features) while promoting data fidelity, and the functional is minimized by solving the Euler-Lagrange equations with an explicit finite-difference scheme. We show the accuracy of our method from a benchmark data set which represents the sea surface temperature off the coast of Oregon, USA. Technical details, such as how to impose continuity across data gaps using inpainting, are also described.

Urbanization, a major driver of global change, profoundly impacts our physical and social world, for example, altering carbon cycling and climate. Understanding these consequences for better scientific insights and effective decision-making unarguably requires accurate information on urban extent and its spatial distributions. In this study, we developed a cluster-based method to estimate the optimal thresholds and map urban extents from the nighttime light remotesensing data, extended this method to the global domain by developing a computational method (parameterization) to estimate the key parameters in the cluster-based method, and built a consistent 20-year global urban map series to evaluate the time-reactive nature of global urbanization (e.g. 2000 in Fig. 1). Supported by urban maps derived from nightlights remotesensing data and socio-economic drivers, we developed an integrated modeling framework to project future urban expansion by integrating a top-down macro-scale statistical model with a bottom-up urban growth model. With the models calibrated and validated using historical data, we explored urban growth at the grid level (1-km) over the next two decades under a number of socio-economic scenarios. The derived spatiotemporal information of historical and potential future urbanization will be of great value with practical implications for developing adaptation and risk management measures for urban infrastructure, transportation, energy, and water systems when considered together with other factors such as climate variability and change, and high impact weather events.

In order to monitor the change of regional ecological environment quality, this paper use MODIS and DMSP / OLS remotesensing data, from the production capacity, external disturbance changes and human socio-economic development of the three main factors affecting the quality of ecosystems, select the net primary productivity, vegetation index and light index, using the principal component analysis method to automatically determine the weight coefficient, construction of the formation of enhanced remotesensing ecological index, and the ecological environment quality of Hainan Island from 2001 to 2013 was monitored and analyzed. The enhanced remotesensing ecological index combines the effects of the natural environment and human activities on ecosystems, and according to the contribution of each principal component automatically determine the weight coefficient, avoid the design of the weight of the parameters caused by the calculation of the human error, which provides a new method for the operational operation of regional macro ecological environment quality monitoring. During the period from 2001 to 2013, the ecological environment quality of Hainan Island showed the characteristics of decend first and then rise, the ecological environment in 2005 was affected by severe natural disasters, and the quality of ecological environment dropped sharply. Compared with 2001, in 2013 about 20000 square kilometers regional ecological environmental quality has improved, about 8760 square kilometers regional ecological environment quality is relatively stable, about 5272 square kilometers regional ecological environment quality has decreased. On the whole, the quality of ecological environment in the study area is good, the frequent occurrence of natural disasters, on the quality of the ecological environment to a certain extent.

Recent research findings on modeling actual evapotranspiration (ET) using remotesensing data and methods have proven the ability of these methods to address wide range of hydrological and water resources issues including river basin water balance for improved water resources management, drought monitoring, drought impact and socioeconomic responses, agricultural water management, optimization of land-use for water conservations, water allocation agreement among others. However, there is still a critical need to identify appropriate type of ET information that can address each of these issues. The current trend of increasing demand for water due to population growth coupled with variable and limited water supply due to drought especially in arid and semiarid regions with limited water supply have highlighted the need for such information. To properly address these issues different spatial and temporal resolutions of ET information will need to be used. For example, agricultural water management applications require ET information at field (30-m) and daily time scales while for river basin hydrologic analysis relatively coarser spatial and temporal scales can be adequate for such regional applications. The objective of this analysis is to evaluate the potential of using an integrated ET information that can be used to address some of these issues collectively. This analysis will highlight efforts to address some of the issues that are applicable to New Mexico including assessment of statewide water budget as well as drought impact and socioeconomic responses which all require ET information but at different spatial and temporal scales. This analysis will provide an evaluation of four remotesensing based ET models including ALEXI, DisALEXI, SSEBop, and SEBAL3.0. The models will be compared with ground-based observations from eddy covariance towers and water balance calculations. Remotesensing data from Landsat, MODIS, and VIIRS sensors will be used to provide ET

Full Text Available Despite being the driest inhabited continent, Australia has one of the highest per capita water consumptions in the world. In addition, instead of having fit-for-purpose water supplies (using different qualities of water for different applications, highly treated drinking water is used for nearly all of Australia’s urban water supply needs, including landscape irrigation. The water requirement of urban landscapes, particularly urban parklands, is of growing concern. The estimation of evapotranspiration (ET and subsequently plant water requirements in urban vegetation needs to consider the heterogeneity of plants, soils, water, and climate characteristics. This research contributes to a broader effort to establish sustainable irrigation practices within the Adelaide Parklands in Adelaide, South Australia. In this paper, two practical ET estimation approaches are compared to a detailed Soil Water Balance (SWB analysis over a one year period. One approach is the Water Use Classification of Landscape Plants (WUCOLS method, which is based on expert opinion on the water needs of different classes of landscape plants. The other is a remotesensing approach based on the Enhanced Vegetation Index (EVI from Moderate Resolution Imaging Spectroradiometer (MODIS sensors on the Terra satellite. Both methods require knowledge of reference ET calculated from meteorological data. The SWB determined that plants consumed 1084 mm·yr−1 of water in ET with an additional 16% lost to drainage past the root zone, an amount sufficient to keep salts from accumulating in the root zone. ET by MODIS EVI was 1088 mm·yr−1, very close to the SWB estimate, while WUCOLS estimated the total water requirement at only 802 mm·yr−1, 26% lower than the SWB estimate and 37% lower than the amount actually added including the drainage fraction. Individual monthly ET by MODIS was not accurate, but these errors were cancelled out to give good agreement on an annual time step. We

Planetary spacecraft are viewed through a troposphere that absorbs and delays radio signals propagating through it. Tropospheric water, in the form of vapor, cloud liquid, and precipitation, emits radio noise which limits satellite telemetry communication link performance. Even at X-band, rain storms have severely affected several satellite experiments including a planetary encounter. The problem will worsen with DSN implementation of Ka-band because communication link budgets will be dominated by tropospheric conditions. Troposphere-induced propagation delays currently limit VLBI accuracy and are significant sources of error for Doppler tracking. Additionally, the success of radio science programs such as satellite gravity wave experiments and atmospheric occultation experiments depends on minimizing the effect of water vapor-induced propagation delays. In order to overcome limitations imposed by the troposphere, the Deep Space Network has supported a program of radiometric remotesensing. Currently, water vapor radiometers (WVRs) and microwave temperature profilers (MTPs) support many aspects of the Deep Space Network operations and research and development programs. Their capability to sense atmospheric water, microwave sky brightness, and atmospheric temperature is critical to development of Ka-band telemetry systems, communication link models, VLBI, satellite gravity wave experiments, and radio science missions. During 1993, WVRs provided data for propagation model development, supported planetary missions, and demonstrated advanced tracking capability. Collection of atmospheric statistics is necessary to model and predict performance of Ka-band telemetry links, antenna arrays, and radio science experiments. Since the spectrum of weather variations has power at very long time scales, atmospheric measurements have been requested for periods ranging from one year to a decade at each DSN site. The resulting database would provide reliable statistics on daily

Full Text Available For mapping, quantifying and monitoring regional and global forest health, satellite remotesensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large quantity, operational applications are still limited by deficits of in situ verification. In situ sampling data as input is required in order to add value to physical imaging remotesensing observations and possibilities to interlink the forest health assessment with biotic and abiotic factors. Numerous methods on how to link remotesensing and in situ data have been presented in the scientific literature using e.g. empirical and physical-based models. In situ data differs in type, quality and quantity between case studies. The irregular subsets of in situ data availability limit the exploitation of available satellite remotesensing data. To achieve a broad implementation of satellite remotesensing data in forest monitoring and management, a standardization of in situ data, workflows and products is essential and necessary for user acceptance. The key focus of the review is a discussion of concept and is designed to bridge gaps of understanding between forestry and remotesensing science community. Methodological approaches for in situ/remote-sensing implementation are organized and evaluated with respect to qualifying for forest monitoring. Research gaps and recommendations for standardization of remote-sensing based products are discussed. Concluding the importance of outstanding organizational work to provide a legally accepted framework for new information products in forestry are highlighted.

Aug 31, 2017 ... to comprehend the tectonic development of the ... software for the analysis and interpretation of G– .... The application of remotesensing for mapping ..... Pf1 and Pf2 show profile locations adopted for joint G–M modelling.

remotesensing techniques particularly those referring to change detection. This kind of ... Technol. depending on many factors in relation to climate conditions, nature .... geomorphologic position make it a perfect wind corridor. (Chahbani ...

Ethiopian Journal of Environmental Studies and Management ... technology provides an efficient avenue of assessment of biomass content of any area. ... use data, can be integrated into GIS together with results from remotesensing analysis ...

Overview of remotesensing of chlorophyll flourescene in ocean waters. ... Besides empirical algorithms with the blue-green ratio, the algorithms based on ... between fluorescence and chlorophyll concentration and the red shift phenomena.

This comprehensive technical overview of the core theory of thermal remotesensing and its applications in hydrology, agriculture, and forestry includes a host of illuminating examples and covers everything from the basics to likely future trends in the field.

Remotesensing data has been used for mapping coastal vegetation along the Goa Coast, India. The study envisages the use of digital image processing techniques for delineating geomorphic features and associated vegetation, including mangrove, along...

DTC) algorithm for classification of remotelysensed satellite data (Landsat TM) using open source support. The decision tree is constructed by recursively partitioning the spectral distribution of the training dataset using WEKA, open source ...

Remotesensing of vegetation function and traits has advanced significantly over the past half-century in the capacity to retrieve useful plant biochemical, physiological and structural quantities across a range of spatial and temporal scales

Soil-moisture interaction and the consequent liberation of ions causes the salinity of waters. The salinity of river, lake, ocean and ground water changes due to seepage and surface runoff. We have studied the feasibility of using microwave remotesensing for the estimation of salinity by carrying out numerical calculations to study the microwave remotesensing responses of various models representative of river, lake and ocean water. The results show the dependence of microwave remotesensing responses on the salinity and surface temperature of water. The results presented in this paper will be useful in the selection of microwave sensor parameters and in the accurate estimation of salinity from microwave remotesensing data

The ASU 100 Cities Project and the ASU Mars Space Flight Facility (MSFF) present JEarth, a set of analytical Geographic Information System (GIS) tools for viewing and processing Earth-based remotesensing imagery and vectors, including high-resolution and hyperspectral imagery such as TIMS and MASTER. JEarth is useful for a wide range of researchers and practitioners who need to access, view, and analyze remotesensing imagery. JEarth stems from existing MSFF applications: the Java application JMars (Java Mission-planning and Analysis for RemoteSensing) for viewing and analyzing remotesensing imagery and THMPROC, a web-based, interactive tool for processing imagery to create band combinations, stretches, and other imagery products. JEarth users can run the application on their desktops by installing Java-based open source software on Windows, Mac, or Linux operating systems.

.... This effort is cooperatively conducted with the professional researchers at the RemoteSensing GIS Center of the US Army Cold Regions Research and Engineering Laboratory in Hanover, New Hampshire...

The application of GMM to remotesensing image classification ... A . The boundary that has a Mahalanobis distance to the centre ... yields the Baye's theorem: ..... bands were extracted using the layer properties tool and visualised in MATLAB ...

National Aeronautics and Space Administration — The proposed innovation is Spark-RS, an open source software project that enables GPU-accelerated remotesensing workflows in an Apache Spark distributed computing...

A GIS AND REMOTESENSING APPROACH TO ASSESSMENT OF DEFORESTATION IN ... This study measured and analyzed deforestation in Uyo and examined the possible effects of the ..... the Burkill technique, (1985, 1994, 1995, 1997.

remotesensing data for Uyo for the periods 1969, 1978, 1988, 2001 and 2004; evaluate the ... geographical information system (GIS) technology was applied to carry out this research. Field ..... preventing erosion, landslides, and making the.

Over the past 30 years, the scientific community has learned a great deal about the Earth as an integrated system. Much of this research has been enabled by the development of remotesensing technologies and their operation from space. Decision makers in many nations have begun to make use of remotesensing data for resource management, policy making, and sustainable development planning. This paper makes an attempt to provide a survey of the current state of the requirements and use of remotesensing for sustainable development in Africa. This activity has shown that there are not many climate data ready decision support tools already functioning in Africa. There are, however, endusers with known requirements who could benefit from remotesensing data.

Blending the most fundamental Remote-Sensing principles (RS) with the most functional spatial knowledge (GIS) with the objective of the determination of the accident-prone palms and points (case study: Tehran-Hamadan Highway on Saveh Superhighway)

In response to the need for improved observations of environmental factors to better understand the links between human health and the environment, NASA has established a new program to significantly improve the utilization of NASA's diverse array of data, information, and observations of the Earth for health applications. This initiative, lead by Goddard Space Flight Center (GSFC) has the following goals: (1) To encourage interdisciplinary research on the relationships between environmental parameters (e.g., rainfall, vegetation) and health, (2) Develop practical early warning systems, (3) Create a unique system for the exchange of Earth science and health data, (4) Provide an investigator field support system for customers and partners, (5) Facilitate a system for observation, identification, and surveillance of parameters relevant to environment and health issues. The NASA Environment and Health Program is conducting several interdisciplinary projects to examine applications of remotesensing data and information to a variety of health issues, including studies on malaria, Rift Valley Fever, St. Louis Encephalitis, Dengue Fever, Ebola, African Dust and health, meningitis, asthma, and filariasis. In addition, the NASA program is creating a user-friendly data system to help provide the public health community with easy and timely access to space-based environmental data for epidemiological studies. This NASA data system is being designed to bring land, atmosphere, water and ocean satellite data/products to users not familiar with satellite data/products, but who are knowledgeable in the Geographic Information Systems (GIS) environment. This paper discusses the most recent results of the interdisciplinary environment-health research projects and provides an analysis of the usefulness of the satellite data to epidemiological studies. In addition, there will be a summary of presently-available NASA Earth science data and a description of how it may be obtained.

Natural hazards like earthquakes can result to enormous property damage, and human casualties in mountainous areas. Italy has always been exposed to numerous earthquakes, mostly concentrated in central and southern regions. Last year, two seismic events near Norcia (central Italy) have occurred, which led to substantial loss of life and extensive damage to properties, infrastructure and cultural heritage. This research utilizes remotesensing products and GIS software, to provide a database of information. We used both SAR images of Sentinel 1A and optical imagery of Landsat 8 to examine the differences of topography with the aid of the multi temporal monitoring technique. This technique suits for the observation of any surface deformation. This database is a cluster of information regarding the consequences of the earthquakes in groups, such as property and infrastructure damage, regional rifts, cultivation loss, landslides and surface deformations amongst others, all mapped on GIS software. Relevant organizations can implement these data in order to calculate the financial impact of these types of earthquakes. In the future, we can enrich this database including more regions and enhance the variety of its applications. For instance, we could predict the future impacts of any type of earthquake in several areas, and design a preliminarily model of emergency for immediate evacuation and quick recovery response. It is important to know how the surface moves, in particular geographical regions like Italy, Cyprus and Greece, where earthquakes are so frequent. We are not able to predict earthquakes, but using data from this research, we may assess the damage that could be caused in the future.

To accurately measure the concentrations of atmospheric gasses, especially the gasses with low concentrations, strong absorption features must be accessed. Each molecular species or constituent has characteristic mid-infrared absorption features by which either column content or range resolved concentrations can be measured. Because of these characteristic absorption features the mid infrared spectral region is known as the fingerprint region. However, as noted by the Decadal Survey, mid-infrared solid-state lasers needed for DIAL systems are not available. The primary reason is associated with short upper laser level lifetimes of mid infrared transitions. Energy gaps between the energy levels that produce mid-infrared laser transitions are small, promoting rapid nonradiative quenching. Nonradiative quenching is a multiphonon process, the more phonons needed, the smaller the effect. More low energy phonons are required to span an energy gap than high energy phonons. Thus, low energy phonon materials have less nonradiative quenching compared to high energy phonon materials. Common laser materials, such as oxides like YAG, are high phonon energy materials, while fluorides, chlorides and bromides are low phonon materials. Work at NASA Langley is focused on a systematic search for novel lanthanide-doped mid-infrared solid-state lasers using both quantum mechanical models (theoretical) and spectroscopy (experimental) techniques. Only the best candidates are chosen for laser studies. The capabilities of modeling materials, experimental challenges, material properties, spectroscopy, and prospects for lanthanide-doped mid-infrared solid-state laser devices will be presented. - Highlights: • We discuss mid infrared lasers and laser materials. • We discuss applications to remotesensing. • We survey the lanthanide ions in low phonon materials for potential. • We present examples of praseodymium mid infrared spectroscopy and laser design.

Oil pollution levels were estimated using simultaneous acquisition of data from remotesensing by helicopter and fluorescence spectroscopy on surface samples. Laboratory quantitative analysis of hydrocarbons was used to calibrate remotelysensed data. The data were treated using a computer to generate a colour-coded map not attainable with conventional methods representing seawater pollution. Results were in good agreement and indicated that remotelysensed data together with those achieved by fluorescence spectroscopy are applicable for monitoring hydrocarbon pollution. (author)

Oil pollution levels were estimated using simultaneous acquisition of data from remotesensing by helicopter and fluorescence spectroscopy on surface samples. Laboratory quantitative analysis of hydrocarbons was used to calibrate remotelysensed data. The data were treated using a computer to generate a colour-coded map not attainable with conventional methods representing seawater pollution. Results were in good agreement and indicated that remotelysensed data together with those achieved by fluorescence spectroscopy are applicable for monitoring hydrocarbon pollution. (author)

The data presented here were originally collected for the article “Frontiers of Urbanization: Identifying and Explaining Urbanization Hot Spots in the South of Mexico City Using Human and Remote Sensing” (Rodriguez et al. 2017) [4]. They were divided into three databases (remotesensing, human sensing, and census information), using a multi-method approach with the goal of analyzing the impact of urbanization on protected areas in southern Mexico City. The remotesensing database was prepared...

Full Text Available Recently, hashing-based large-scale remotesensing (RS image retrieval has attracted much attention. Many new hashing algorithms have been developed and successfully applied to fast RS image retrieval tasks. However, there exists an important problem rarely addressed in the research literature of RS image hashing. The RS images are practically produced in a streaming manner in many real-world applications, which means the data distribution keeps changing over time. Most existing RS image hashing methods are batch-based models whose hash functions are learned once for all and kept fixed all the time. Therefore, the pre-trained hash functions might not fit the ever-growing new RS images. Moreover, the batch-based models have to load all the training images into memory for model learning, which consumes many computing and memory resources. To address the above deficiencies, we propose a new online hashing method, which learns and adapts its hashing functions with respect to the newly incoming RS images in terms of a novel online partial random learning scheme. Our hash model is updated in a sequential mode such that the representative power of the learned binary codes for RS images are improved accordingly. Moreover, benefiting from the online learning strategy, our proposed hashing approach is quite suitable for scalable real-world remotesensing image retrieval. Extensive experiments on two large-scale RS image databases under online setting demonstrated the efficacy and effectiveness of the proposed method.

Information regarding the magnitude and distribution of PM(sub 2.5) emissions is crucial in establishing effective PM regulations and assessing the associated risk to human health and the ecosystem. At present, emission data is obtained from measured or estimated emission factors of various source types. Collecting such information for every known source is costly and time consuming. For this reason, emission inventories are reported periodically and unknown or smaller sources are often omitted or aggregated at large spatial scale. To address these limitations, we have developed and evaluated a novel method that uses remotesensing data to construct spatially-resolved emission inventories for PM(sub 2.5). This approach enables us to account for all sources within a fixed area, which renders source classification unnecessary. We applied this method to predict emissions in the northeast United States during the period of 2002-2013 using high- resolution 1 km x 1 km Aerosol Optical Depth (AOD). Emission estimates moderately agreed with the EPA National Emission Inventory (R(sup2) = 0.66 approx. 0.71, CV = 17.7 approx. 20%). Predicted emissions are found to correlate with land use parameters suggesting that our method can capture emissions from land use-related sources. In addition, we distinguished small-scale intra-urban variation in emissions reflecting distribution of metropolitan sources. In essence, this study demonstrates the great potential of remotesensing data to predict particle source emissions cost-effectively.

The Kyoto Protocol to the United Nations Framework Convention on Climate Change contains quantified, legally binding commitments to limit or reduce greenhouse gas emissions to 1990 levels and allows carbon emissions to be balanced by carbon sinks represented by vegetation. The issue of using vegetation cover as an emission offset raises a debate about the adequacy of current remotesensing systems and data archives to both assess carbon stocks/sinks at 1990 levels, and monitor the current and future global status of those stocks. These concerns and the potential ratification of the Protocol among participating countries is stimulating policy debates and underscoring a need for the exchange of information between the international legal community and the remotesensing community. On October 20-22 1999, two working groups of the International Society for Photogrammetry and RemoteSensing (ISPRS) joined with the University of Michigan (Michigan, USA) to convene discussions on how remotesensing technology could contribute to the information requirements raised by implementation of, and compliance with, the Kyoto Protocol. The meeting originated as a joint effort between the Global Monitoring Working Group and the Radar Applications Working Group in Commission VII of the ISPRS, co-sponsored by the University of Michigan. Tile meeting was attended by representatives from national government agencies and international organizations and academic institutions. Some of the key themes addressed were: (1) legal aspects of transnational remotesensing in the context of the Kyoto Protocol; (2) a review of the current and future and remotesensing technologies that could be applied to the Kyoto Protocol; (3) identification of areas where additional research is needed in order to advance and align remotesensing technology with the requirements and expectations of the Protocol; and 94) the bureaucratic and research management approaches needed to align the remotesensing

This paper explores the potential for airborne remotesensing for atmospheric sciences research. Passive and active techniques from the microwave to visible bands are discussed. It is concluded that technology has progressed sufficiently in several areas that the time is right to develop and operate new remotesensing instruments for use by the community of atmospheric scientists as general purpose tools. Promising candidates include Doppler radar and lidar, infrared short range radiometry, and microwave radiometry.

The current status of artificial intelligence AI technology is discussed along with imagery data management, database interrogation, and decision making. Techniques adapted from the field of artificial intelligence (AI) have significant, wide ranging impacts upon computer-assisted remotesensing analysis. AI based techniques offer a powerful and fundamentally different approach to many remotesensing tasks. In addition to computer assisted analysis, AI techniques can also aid onboard spacecraft data processing and analysis and database access and query.

Full Text Available M B E R 2 0 0 8 15 USING REMOTELY- SENSED DATA FOR OPTIMAL FIELD SAMPLING BY DR PRAVESH DEBBA STATISTICS IS THE SCIENCE pertaining to the collection, summary, analysis, interpretation and presentation of data. It is often impractical... studies are: where to sample, what to sample and how many samples to obtain. Conventional sampling techniques are not always suitable in environmental studies and scientists have explored the use of remotely-sensed data as ancillary information to aid...

The primary agricultural objective of this research is to determine what soil and crop information can be verified from remotelysensed images during the growing season. Specifically: (1) Elements of crop stress due to drought, weeds, disease and nutrient deficiencies will be documented with ground truth over specific agricultural sites and (2) Use of remotesensing with GPS and GIS technology for providing a safe and environmentally friendly application of fertilizers and chemicals will be documented.

Full Text Available Africa. 2Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Lynwood Road, Pretoria 0002, South Africa. 3Tshwane University of Technology, Pretoria 0001, South Africa. ABSTRACT A mobile LIDAR (LIght Detection... obtained using the CSIR-NLC mobile LIDAR in a 23 hour field campaign at the University of Pretoria. Index Terms— Atmospheric measurements, Remotesensing, Aerosols, Air pollution, Meteorology 1. INTRODUCTION Remotesensing is a technique...

Contaminants in the snow can be used to reflect regional and global environmental pollution caused by human activities. However, so far, the research on space-time monitoring of snow contamination concentration for a wide range or areas difficult for human to reach is very scarce. In the present paper, based on the simulated atmospheric deposition experiments, the spectroscopy technique method was applied to analyze the effect of different contamination concentration on the snow reflectance spectra. Then an evaluation of snow contamination concentration (SCC) retrieval methods was conducted using characteristic index method (SDI), principal component analysis (PCA), BP neural network and RBF neural network method, and the estimate effects of four methods were compared. The results showed that the neural network model combined with hyperspectral remotesensing data could estimate the SCC well.

Full Text Available Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surfaceÃ¢Â€Â“atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remotesensing procedures. Remotesensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remotesensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF and Particle filter (PF, for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law and could be a strong alternative to EnKF which is subject to some

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remotesensing procedures. Remotesensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remotesensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear

In this chapter the author critically examines the prospects for reducing uncertainties over global biomass burning using remotesensing. First he considers the global temporal, spatial, and intensity distributions of fires and the remotely sensible signals they create and discusses the opportunities and problems that exist for matching available sensors to fire signal. Then he considers problems relating to instrumentation and to atmospheric interference

This report presents the results of the EU project "Carbon and water fluxes of Mediterranean forests and impacts of land use/cover changes". The objectives of the project can be summarized as follows: (I) surface energy balance mapping using remotesensing, (ii) carbon uptake mapping using remote

Terminology is a key issue for a better understanding among people using various languages. Terminology accuracy is essential during all phases of international cooperation. It is crucial to keep up with the latest quantitative and qualitative developments and novelties of the terminology in advanced technology fields such as aerospace science and industry. This is especially true in remotesensing and geoinformatics which develop rapidly and have wide and ever extending applications in various domains of human activity. The importance of the correct use of remotesensing terms refers not only to people working in this field but also to experts in many disciplines who handle remotesensing data and information products. The paper is devoted to terminology issues that refer to all aspects of remotesensing research and application areas. The attention is drawn on the recent needs and peculiarities of compiling specialized dictionaries in the subject area of remotesensing. Details are presented about the work in progress on the preparation of an English-Bulgarian dictionary of remotesensing terms focusing on Earth observations and geoinformation science. Our belief is that the elaboration of bilingual and multilingual dictionaries and glossaries in this spreading, most technically advanced and promising field of human expertise is of great practical importance. Any interest in cooperation and initiating of suchlike collaborative multilingual projects is welcome and highly appreciated.

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remotesensing data. Currently, remotesensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remotesensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remotesensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remotesensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remotesensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

Full Text Available Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remotesensing data. Currently, remotesensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remotesensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remotesensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remotesensing-based spatially-explicit comprehensive ecosystem health system are: (1 scale issue; (2 transportability issue; (3 data availability; and (4 uncertainties in health indicators estimated from remotesensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

In China it is important to explore coal prospecting by taking advantage of modern remotesensing and geographic information system technologies. Given a theoretical basis for coal prospecting by remotesensing, the methodologies and existing problems are demonstrated systematically by summarizing past practices of coal prospecting with remotesensing. A new theory of coal prospecting with remotesensing is proposed. In uncovered areas, coal resources can be prospected by direct interpretation. In coal bearing strata of developed areas covered by thin Quaternary strata or vegetation, prospecting for coal can be carried out by indirect interpretation of geomorphology and vegetation. For deeply buried underground deposits, coal prospecting can rely on tectonic structures, interpretation and analysis of new tectonic clues and regularity of coal formation and preservation controlled by tectonic structures. By applying newly hyper-spectral, multi-polarization, multi-angle, multi-temporal and multi-resolution remotesensing data and carrying out integrated analysis of geographic attributes, ground attributes, geophysical exploration results, geochemical exploration results, geological drilling results and remotesensing data by GIS tools, coal geology resources and mineralogical regularities can be explored and coal resource information can be acquired with some confidence. 12 refs., 4 figs., 3 tabs.

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remotesensing data. Currently, remotesensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remotesensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remotesensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remotesensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remotesensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges. PMID:25386759

Remotesensing is the use of electromagnetic energy to measure the physical properties of distant objects. It includes photography and geophysical surveying as well as newer techniques that use other parts of the electromagnetic spectrum. The history of remotesensing begins with photography. The origin of other types of remotesensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors have been designed to operate in virtually all of the electromagnetic spectrum. Today a wide variety of remotesensing instruments are available for use in hydrological studies; satellite data, such as Skylab photographs and Landsat images are particularly suitable for regional problems and studies. Planned future satellites will provide a ground resolution of 10–80 m. Remotesensing is currently used for hydrological applications in most countries of the world. The range of applications includes groundwater exploration determination of physical water quality, snowfield mapping, flood-inundation delineation, and making inventories of irrigated land. The use of remotesensing commonly results in considerable hydrological information at minimal cost. This information can be used to speed-up the development of water resources, to improve management practices, and to monitor environmental problems.

Earthquakes often cause destructive and unpredictable changes that can affect local hydrology (e.g. groundwater elevation or reduction) and thus disrupt land uses and human activities. Prolific agricultural regions overlie seismically active areas, emphasizing the importance to improve our understanding and monitoring of hydrologic and agricultural systems following a seismic event. A thorough data collection is necessary for adequate post-earthquake crop management response; however, the large spatial extent of earthquake's impact makes challenging the collection of robust data sets for identifying locations and magnitude of these impacts. Observing hydrologic responses to earthquakes is not a novel concept, yet there is a lack of methods and tools for assessing earthquake's impacts upon the regional hydrology and agricultural systems. The objective of this paper is to describe how remotesensing imagery, methods and tools allow detecting crop responses and damage incurred after earthquakes because a change in the regional hydrology. Many remotesensing datasets are long archived with extensive coverage and with well-documented methods to assess plant-water relations. We thus connect remotesensing of plant water relations to its utility in agriculture using a post-earthquake agrohydrologic remotesensing (PEARS) framework; specifically in agro-hydrologic relationships associated with recent earthquake events that will lead to improved water management. - Highlights: • Remotesensing to improve agricultural disaster management • Introduce post-earthquake agrohydrologic remotesensing (PEARS) framework • Apply PEARS framework to 2010 Maule Earthquake in Central Chile

Insect disturbance are important agents of change in forest ecosystems around the globe, yet their spatial and temporal distribution and dynamics are not well understood. Remotesensing has gained much attention in mapping and understanding insect outbreak dynamics. Consequently, we here review the current literature on the remotesensing of insect disturbances. We suggest to group studies into three insect types: bark beetles, broadleaved defoliators, and coniferous defoliators. By so doing, we systematically compare the sensors and methods used for mapping insect disturbances within and across insect types. Results suggest that there are substantial differences between methods used for mapping bark beetles and defoliators, and between methods used for mapping broadleaved and coniferous defoliators. Following from this, we highlight approaches that are particularly suited for each insect type. Finally, we conclude by highlighting future research directions for remotesensing of insect disturbances. In particular, we suggest to: 1) Separate insect disturbances from other agents; 2) Extend the spatial and temporal domain of analysis; 3) Make use of dense time series; 4) Operationalize near-real time monitoring of insect disturbances; 5) Identify insect disturbances in the context of coupled human-natural systems; and 6) Improve reference data for assessing insect disturbances. Since the remotesensing of insect disturbances has gained much interest beyond the remotesensing community recently, the future developments identified here will help integrating remotesensing products into operational forest management. Furthermore, an improved spatiotemporal quantification of insect disturbances will support an inclusion of these processes into regional to global ecosystem models.

The relative motion between remotesensing satellite sensor and objects is one of the most common reasons for remotesensing image degradation. It seriously weakens image data interpretation and information extraction. In practice, point spread function (PSF) should be estimated firstly for image restoration. Identifying motion blur direction and length accurately is very crucial for PSF and restoring image with precision. In general, the regular light-and-dark stripes in the spectrum can be employed to obtain the parameters by using Radon transform. However, serious noise existing in actual remotesensing images often causes the stripes unobvious. The parameters would be difficult to calculate and the error of the result relatively big. In this paper, an improved motion blur parameter identification method to noisy remotesensing image is proposed to solve this problem. The spectrum characteristic of noisy remotesensing image is analyzed firstly. An interactive image segmentation method based on graph theory called GrabCut is adopted to effectively extract the edge of the light center in the spectrum. Motion blur direction is estimated by applying Radon transform on the segmentation result. In order to reduce random error, a method based on whole column statistics is used during calculating blur length. Finally, Lucy-Richardson algorithm is applied to restore the remotesensing images of the moon after estimating blur parameters. The experimental results verify the effectiveness and robustness of our algorithm.

Earthquakes often cause destructive and unpredictable changes that can affect local hydrology (e.g. groundwater elevation or reduction) and thus disrupt land uses and human activities. Prolific agricultural regions overlie seismically active areas, emphasizing the importance to improve our understanding and monitoring of hydrologic and agricultural systems following a seismic event. A thorough data collection is necessary for adequate post-earthquake crop management response; however, the large spatial extent of earthquake's impact makes challenging the collection of robust data sets for identifying locations and magnitude of these impacts. Observing hydrologic responses to earthquakes is not a novel concept, yet there is a lack of methods and tools for assessing earthquake's impacts upon the regional hydrology and agricultural systems. The objective of this paper is to describe how remotesensing imagery, methods and tools allow detecting crop responses and damage incurred after earthquakes because a change in the regional hydrology. Many remotesensing datasets are long archived with extensive coverage and with well-documented methods to assess plant-water relations. We thus connect remotesensing of plant water relations to its utility in agriculture using a post-earthquake agrohydrologic remotesensing (PEARS) framework; specifically in agro-hydrologic relationships associated with recent earthquake events that will lead to improved water management. - Highlights: • Remotesensing to improve agricultural disaster management • Introduce post-earthquake agrohydrologic remotesensing (PEARS) framework • Apply PEARS framework to 2010 Maule Earthquake in Central Chile.

Full Text Available The classes in fuzzy classification schemes are defined as fuzzy sets, partitioning the feature space through fuzzy rules, defined by fuzzy membership functions. Applying fuzzy classification schemes in remotesensing allows each pixel or segment to be an incomplete member of more than one class simultaneously, i.e., one that does not fully meet all of the classification criteria for any one of the classes and is member of more than one class simultaneously. This can lead to fuzzy, ambiguous and uncertain class assignation, which is unacceptable for many applications, indicating the need for a reliable defuzzification method. Defuzzification in remotesensing has to date, been performed by “crisp-assigning” each fuzzy-classified pixel or segment to the class for which it best fulfills the fuzzy classification rules, regardless of its classification fuzziness, uncertainty or ambiguity (maximum method. The defuzzification of an uncertain or ambiguous fuzzy classification leads to a more or less reliable crisp classification. In this paper the most common parameters for expressing classification uncertainty, fuzziness and ambiguity are analysed and discussed in terms of their ability to express the reliability of a crisp classification. This is done by means of a typical practical example from Object Based Image Analysis (OBIA.

Full Text Available Land cover classification has been widely investigated in remotesensing for agricultural, ecological and hydrological applications. Landsat images with multispectral bands are commonly used to study the numerous classification methods in order to improve the classification accuracy. Thermal remotesensing provides valuable information to investigate the effectiveness of the thermal bands in extracting land cover patterns. k-NN and Random Forest algorithms were applied to both the single Landsat 8 image and the time series Landsat 4/5 images for the Attert catchment in the Grand Duchy of Luxembourg, trained and validated by the ground-truth reference data considering the three level classification scheme from COoRdination of INformation on the Environment (CORINE using the 10-fold cross validation method. The accuracy assessment showed that compared to the visible and near infrared (VIS/NIR bands, the time series of thermal images alone can produce comparatively reliable land cover maps with the best overall accuracy of 98.7% to 99.1% for Level 1 classification and 93.9% to 96.3% for the Level 2 classification. In addition, the combination with the thermal band improves the overall accuracy by 5% and 6% for the single Landsat 8 image in Level 2 and Level 3 category and provides the best classified results with all seven bands for the time series of Landsat TM images.

overestimation of 0.08, which was attributed to PAR absorption by soil that could not be excluded from the fAPAR calculation. This research clearly demonstrates that high spectral and spatial resolution remotesensing VIs can be used to successfully model Arctic biophysical variables. The methods and results presented in this research provided a guide for future studies aiming to model other Arctic biophysical variables through remotesensing data.

Full Text Available With the arrival of the big data era in Earth observation, the remotesensing communities have accumulated a large amount of invaluable and irreplaceable data for global monitoring. These massive remotesensing data have enabled large-area and long-term series Earth observation, and have, in particular, made standard, automated product generation more popular. However, there is more than one type of data selection for producing a certain remotesensing product; no single remote sensor can cover such a large area at one time. Therefore, we should automatically select the best data source from redundant multisource remotesensing data, or select substitute data if data is lacking, during the generation of remotesensing products. However, the current data selection strategy mainly adopts the empirical model, and has a lack of theoretical support and quantitative analysis. Hence, comprehensively considering the spectral characteristics of ground objects and spectra differences of each remote sensor, by means of spectrum simulation and correlation analysis, we propose a suitability evaluation model for product generation. The model will enable us to obtain the Production Suitability Index (PSI of each remotesensing data. In order to validate the proposed model, two typical value-added information products, NDVI and NDWI, and two similar or complementary remote sensors, Landsat-OLI and HJ1A-CCD1, were chosen, and the verification experiments were performed. Through qualitative and quantitative analysis, the experimental results were consistent with our model calculation results, and strongly proved the validity of the suitability evaluation model. The proposed production suitability evaluation model could assist with standard, automated, serialized product generation. It will play an important role in one-station, value-added information services during the big data era of Earth observation.

International cooperation in the U.S. Space Program is discussed and related to the NASA program for remotesensing of the earth. Satellite remotesensing techniques are considered along with the selection of the best sensors and wavelength bands. The technology of remotesensing satellites is considered with emphasis on the Landsat system configuration. Future aspects of remotesensing satellites are considered.

A Trihedral Corner Reflector (TCR) is formed by three mutually orthogonal metal plates of various shapes and is a very important scattering structure since it exhibits a high monostatic Radar Cross Section (RCS) over a wide angular range. Moreover it is a handy passive device with low manufacturing costs and robust geometric construction, the maintenance of its efficiency is not difficult and expensive, and it can be used in all weather conditions (i.e., fog, rain, smoke, and dusty environment). These characteristics make it suitable as reference target and radar enhancement device for satellite- and ground-based microwave remotesensing techniques. For instance, TCRs have been recently employed to improve the signal-to-noise ratio of the backscattered signal in the case of urban ground deformation monitoring [1] and dynamic survey of civil infrastructures without natural corners as the Musmeci bridge in Basilicata, Italy [2]. The region of interest for the calculation of TCR's monostatic RCS is here confined to the first quadrant containing the boresight direction. The backscattering term is presented in closed form by evaluating the far-field scattering integral involving the contributions related to the direct illumination and the internal bouncing mechanisms. The Geometrical Optics (GO) laws allow one to determine the field incident on each TCR plate and the patch (integration domain) illuminated by it, thus enabling the use of a Physical Optics (PO) approximation for the corresponding surface current densities to consider for integration on each patch. Accordingly, five contributions are associated to each TCR plate: one contribution is due to the direct illumination of the whole internal surface; two contributions originate by the impinging rays that are simply reflected by the other two internal surfaces; and two contributions are related to the impinging rays that undergo two internal reflections. It is useful to note that the six contributions due to the

We discuss the evolution and state-of-the-art of the use of Unmanned Aerial Systems (UAS) in the field of Photogrammetry and RemoteSensing (PaRS). UAS, Remotely-Piloted Aerial Systems, Unmanned Aerial Vehicles or simply, drones are a hot topic comprising a diverse array of aspects including technology, privacy rights, safety and regulations, and even war and peace. Modern photogrammetry and remotesensing identified the potential of UAS-sourced imagery more than thirty years ago. In the last...

This publication identifies some of the general concepts of remotesensing and explains the image collection process and computer-generated reconstruction of the data. Monitoring the ecological collapse in coral reefs, weather phenomena like El Nino/La Nina, and U.S. Space Shuttle-based sensing projects are some of the areas for which remote…

The geometric outline of remotesensing image data, the so called footprint, can be represented as a number of coordinate tuples. These polygons are associated with according attribute information such as orbit name, ground- and image resolution, solar longitude and illumination conditions to generate a powerful base for classification of planetary experiment data. Speed, handling and extended capabilites are the reasons for using geodatabases to store and access these data types. Techniques for such a spatial database of footprint data are demonstrated using the Relational Database Management System (RDBMS) PostgreSQL, spatially enabled by the PostGIS extension. Exemplary, footprints of the HRSC and OMEGA instruments, both onboard ESA's Mars Express Orbiter, are generated and connected to attribute information. The aim is to provide high-resolution footprints of the OMEGA instrument to the science community for the first time and make them available for web-based mapping applications like the "Planetary Interactive GIS-on-the-Web Analyzable Database" (PIG- WAD), produced by the USGS. Map overlays with HRSC or other instruments like MOC and THEMIS (footprint maps are already available for these instruments and can be integrated into the database) allow on-the-fly intersection and comparison as well as extended statistics of the data. Footprint polygons are generated one by one using standard software provided by the instrument teams. Attribute data is calculated and stored together with the geometric information. In the case of HRSC, the coordinates of the footprints are already available in the VICAR label of each image file. Using the VICAR RTL and PostgreSQL's libpq C library they are loaded into the database using the Well-Known Text (WKT) notation by the Open Geospatial Consortium, Inc. (OGC). For the OMEGA instrument, image data is read using IDL routines developed and distributed by the OMEGA team. Image outlines are exported together with relevant attribute